兼容数据修复

* 写入 Atlas 时加优先级（Texel 级竞争）
消除接缝模糊、局部染色
每个 texel 只相信“最正面”的那个视图。

.gitignore

@@ -37,3 +37,77 @@ CMakeSettings.json
 out/
 bin*/
 make*/
+libs/MVS/texture_output/texture_0.png
+libs/MVS/texture_output/texture_1.png
+libs/MVS/texture_output/texture_2.png
+libs/MVS/texture_output/texture_3.png
+libs/MVS/texture_output/texture_4.png
+libs/MVS/texture_output/texture_5.png
+libs/MVS/texture_output/texture_6.png
+libs/MVS/texture_output/texture_7.png
+libs/MVS/texture_output/texture_8.png
+libs/MVS/texture_output/texture_9.png
+libs/MVS/texture_output/texture_10.png
+libs/MVS/texture_output/texture_11.png
+libs/MVS/texture_output/texture_12.png
+libs/MVS/texture_output/texture_13.png
+libs/MVS/texture_output/texture_14.png
+libs/MVS/texture_output/texture_15.png
+libs/MVS/texture_output/texture_16.png
+libs/MVS/texture_output/texture_17.png
+libs/MVS/texture_output/texture_18.png
+libs/MVS/texture_output/texture_19.png
+libs/MVS/texture_output/texture_20.png
+libs/MVS/texture_output/texture_21.png
+libs/MVS/texture_output/texture_22.png
+libs/MVS/texture_output/texture_23.png
+libs/MVS/texture_output/texture_24.png
+libs/MVS/texture_output/texture_25.png
+libs/MVS/texture_output/texture_26.png
+libs/MVS/texture_output/texture_27.png
+libs/MVS/texture_output/texture_28.png
+libs/MVS/texture_output/texture_29.png
+libs/MVS/texture_output/texture_30.png
+libs/MVS/texture_output/texture_31.png
+libs/MVS/texture_output/texture_32.png
+libs/MVS/texture_output/texture_33.png
+libs/MVS/texture_output/texture_34.png
+libs/MVS/texture_output/texture_35.png
+texture_output/texture_0.png
+color_change/__pycache__/calib_io.cpython-310.pyc
+.gitignore
+.gitignore
+color_change/__pycache__/calibration.cpython-310.pyc
+fill2sc/632723.jpg
+fill2sc/632723.mtl
+fill2sc/fill_preview.png
+fill2sc/fill_report.txt
+.gitignore
+fill2sc/index.txt
+fill2sc/data/20261102/20261102_glasses_vertices.txt
+fill2sc/data/20261102/20261102_glasses.blend
+fill2sc/data/20261102/20261102.mtl
+fill2sc/data/20261102/20261102.zip
+fill2sc/data/20261102/20261102Tex1.jpg
+fill2sc/data/20261102/fill_20261102_preview.png
+fill2sc/data/20261102/index.txt
+fill2sc/data/631230/631230_glasses_vertices.txt
+fill2sc/data/631230/631230_glasses.blend
+fill2sc/data/631230/631230.jpg
+fill2sc/data/631230/631230.mtl
+fill2sc/data/631230/631230.zip
+fill2sc/data/631230/fill_631230_preview.png
+fill2sc/data/631230/index.txt
+fill2sc/data/632723/632723.jpg
+fill2sc/data/632723/632723.mtl
+fill2sc/data/632723/fill_632723_preview.png
+fill2sc/data/632723/fill_preview.png
+fill2sc/data/632723/fill_report.txt
+fill2sc/data/632723/index.txt
+fill2sc/data/636394/636394_glasses_vertices.txt
+fill2sc/data/636394/636394_glasses.blend
+fill2sc/data/636394/636394.jpg
+fill2sc/data/636394/636394.mtl
+fill2sc/data/636394/636394.zip
+fill2sc/data/636394/fill_636394_preview.png
+fill2sc/data/636394/index.txt

CMakeLists.txt

@@ -52,7 +52,7 @@ ENDIF()
 PROJECT(OpenMVS)
 
 SET(ENV_PYTHON_PATH "/home/algo/.conda/envs/py310_pyt210/lib/python3.10/site-packages")
-SET(ENV_MVS_PATH "/home/algo/Documents/openMVS/openMVS/libs/MVS")
+SET(ENV_MVS_PATH "/home/algo/Documents/openMVS/openMVS.ManualUV/libs/MVS")
 
 CONFIGURE_FILE(
   "${CMAKE_CURRENT_SOURCE_DIR}/build/Templates/ConfigEnv.h.in"

apps/TextureMesh/TextureMesh.cpp

@@ -94,6 +94,9 @@ unsigned nMaxThreads;
 int nMaxTextureSize;
 String strExportType;
 String strConfigFileName;
+bool bUseExistingUV;
+String strUVMeshFileName;
+
 boost::program_options::variables_map vm;
 } // namespace OPT
 
@@ -163,6 +166,8 @@ bool Application::Initialize(size_t argc, LPCTSTR* argv)
 		("image-folder", boost::program_options::value<std::string>(&OPT::strImageFolder)->default_value(COLMAP_IMAGES_FOLDER), "folder to the undistorted images")
 		("origin-faceview", boost::program_options::value(&OPT::bOriginFaceview)->default_value(false), "use origin faceview selection")
 		("id", boost::program_options::value(&OPT::nID)->default_value(0), "id")
+		("use-existing-uv", boost::program_options::value(&OPT::bUseExistingUV)->default_value(false), "use existing UV coordinates from the input mesh")
+    	("uv-mesh-file", boost::program_options::value<std::string>(&OPT::strUVMeshFileName), "mesh file with pre-computed UV coordinates")
 		;
 
 	// hidden options, allowed both on command line and
@@ -1134,12 +1139,12 @@ int main(int argc, LPCTSTR* argv)
 	if (!scene.TextureMesh(OPT::nResolutionLevel, OPT::nMinResolution, OPT::minCommonCameras, OPT::fOutlierThreshold, OPT::fRatioDataSmoothness,
 						   OPT::bGlobalSeamLeveling, OPT::bLocalSeamLeveling, OPT::nTextureSizeMultiple, OPT::nRectPackingHeuristic, Pixel8U(OPT::nColEmpty),
 						   OPT::fSharpnessWeight, OPT::nIgnoreMaskLabel, OPT::nMaxTextureSize, views, baseFileName, OPT::bOriginFaceview, 
-						   OPT::strInputFileName, OPT::strMeshFileName))
+						   OPT::strInputFileName, OPT::strMeshFileName, OPT::bUseExistingUV, OPT::strUVMeshFileName))
 		return EXIT_FAILURE;
 	VERBOSE("Mesh texturing completed: %u vertices, %u faces (%s)", scene.mesh.vertices.GetSize(), scene.mesh.faces.GetSize(), TD_TIMER_GET_FMT().c_str());
 
 	// save the final mesh
-	scene.mesh.Save(baseFileName+OPT::strExportType);
+	scene.mesh.Save(baseFileName+OPT::strExportType,cList<String>(),true,OPT::bUseExistingUV);
 	#if TD_VERBOSE != TD_VERBOSE_OFF
 	if (VERBOSITY_LEVEL > 2)
 		scene.ExportCamerasMLP(baseFileName+_T(".mlp"), baseFileName+OPT::strExportType);

color_change/calib_io.py

@@ -0,0 +1,265 @@
+from __future__ import annotations
+
+import json
+import os
+from collections.abc import Callable, Mapping
+from dataclasses import dataclass
+from pathlib import Path, PurePosixPath, PureWindowsPath
+from typing import TypeAlias
+
+import numpy as np
+import pycolmap
+from numpy.typing import NDArray
+from PIL import Image
+
+
+@dataclass(frozen=True, slots=True)
+class CalibrationView:
+    image_id: int
+    name: str
+    rgb_path: Path
+    mask_path: Path
+    camera: pycolmap.Camera
+    cam_from_world: "pycolmap.Rigid3d"
+
+
+@dataclass(frozen=True, slots=True)
+class CalibrationDataset:
+    reconstruction: pycolmap.Reconstruction
+    views: tuple[CalibrationView, ...]
+
+
+@dataclass(frozen=True, slots=True)
+class CalibrationResult:
+    corrected_images: Mapping[int, NDArray[np.uint8]]
+    report: Mapping[str, object]
+
+
+@dataclass(frozen=True, slots=True)
+class CalibrationOutputs:
+    image_paths: tuple[Path, ...]
+    report_path: Path
+
+
+Calibrator: TypeAlias = Callable[[CalibrationDataset], CalibrationResult]
+
+
+def _relative_image_path(name: str) -> Path:
+    posix_path = PurePosixPath(name)
+    windows_path = PureWindowsPath(name)
+    if (
+        not posix_path.parts
+        or posix_path.is_absolute()
+        or ".." in posix_path.parts
+        or windows_path.is_absolute()
+        or windows_path.drive
+        or ".." in windows_path.parts
+    ):
+        raise ValueError(f"unsafe COLMAP image name: {name}")
+    return Path(*posix_path.parts)
+
+
+def _validate_output_target(
+    output_root: Path,
+    resolved_output_root: Path,
+    relative: Path,
+    seen_targets: set[str],
+    seen_ancestors: set[str],
+) -> Path:
+    target = output_root / relative
+    resolved_target = target.resolve()
+    if not resolved_target.is_relative_to(resolved_output_root):
+        raise ValueError(f"output path escapes output_dir: {target}")
+    ancestor = target.parent
+    while True:
+        if (ancestor.exists() or ancestor.is_symlink()) and not ancestor.is_dir():
+            raise NotADirectoryError(f"output ancestor is not a directory: {ancestor}")
+        if ancestor == output_root:
+            break
+        ancestor = ancestor.parent
+    target_key = os.path.normcase(str(resolved_target))
+    if target_key in seen_targets:
+        raise ValueError(f"duplicate output path: {target}")
+    if target_key in seen_ancestors or any(
+        os.path.normcase(str(parent)) in seen_targets
+        for parent in resolved_target.parents
+    ):
+        raise ValueError(f"conflicting output path: {target}")
+    seen_targets.add(target_key)
+    seen_ancestors.update(
+        os.path.normcase(str(parent)) for parent in resolved_target.parents
+    )
+    if target.exists() or target.is_symlink():
+        raise FileExistsError(f"output already exists: {target}")
+    return target
+
+
+def load_dataset(
+    model_dir: str | os.PathLike[str],
+    rgb_dir: str | os.PathLike[str],
+    mask_dir: str | os.PathLike[str],
+) -> CalibrationDataset:
+    # ✅ pycolmap 4.x: 直接用 Reconstruction 加载模型目录
+    reconstruction = pycolmap.Reconstruction(str(Path(model_dir)))
+    
+    # 验证加载成功
+    if reconstruction.num_images() == 0:
+        raise RuntimeError(
+            f"Failed to load COLMAP model from {model_dir}. "
+            f"Expected cameras.bin/images.bin/points3D.bin or .txt files."
+        )
+    
+    print(f"Loaded COLMAP model: {reconstruction.num_images()} images, "
+          f"{reconstruction.num_cameras()} cameras, "
+          f"{reconstruction.num_points3D()} 3D points")
+
+    rgb_root = Path(rgb_dir)
+    mask_root = Path(mask_dir)
+    seen_names: set[str] = set()
+    views: list[CalibrationView] = []
+
+    # ✅ pycolmap 4.x: images 是一个 dict-like 对象
+    for image_id, image in reconstruction.images.items():
+        if not image.has_pose:
+            raise ValueError(f"COLMAP image has no pose: {image.name}")
+
+        relative = _relative_image_path(image.name)
+        normalized_name = os.path.normcase(str(relative))
+        if normalized_name in seen_names:
+            raise ValueError(f"duplicate COLMAP image name: {image.name}")
+        seen_names.add(normalized_name)
+
+        rgb_path = rgb_root / relative
+        mask_path = mask_root / relative.with_suffix(".png")
+        if not rgb_path.is_file():
+            raise FileNotFoundError(f"RGB image not found: {rgb_path}")
+        if not mask_path.is_file():
+            raise FileNotFoundError(f"mask image not found: {mask_path}")
+
+        camera = image.camera
+        if camera is None:
+            raise ValueError(f"COLMAP image has no camera: {image.name}")
+        
+        with Image.open(rgb_path) as rgb_image:
+            rgb_size = rgb_image.size
+        with Image.open(mask_path) as mask_image:
+            mask_size = mask_image.size
+        camera_size = (camera.width, camera.height)
+        if rgb_size != camera_size or mask_size != camera_size:
+            raise ValueError(
+                f"size mismatch for {image.name}: camera={camera_size}, "
+                f"rgb={rgb_size}, mask={mask_size}"
+            )
+
+        views.append(
+            CalibrationView(
+                image_id=image_id,
+                name=image.name,
+                rgb_path=rgb_path,
+                mask_path=mask_path,
+                camera=camera,
+                # ✅ pycolmap 4.x: cam_from_world 是属性，不是方法
+                cam_from_world=image.cam_from_world,
+            )
+        )
+
+    return CalibrationDataset(reconstruction=reconstruction, views=tuple(views))
+
+
+def read_rgb(view: CalibrationView) -> NDArray[np.uint8]:
+    with Image.open(view.rgb_path) as image:
+        return np.array(image.convert("RGB"), dtype=np.uint8, copy=True)
+
+
+def read_mask(view: CalibrationView) -> NDArray[np.bool_]:
+    with Image.open(view.mask_path) as image:
+        return np.array(image.convert("L"), dtype=np.uint8, copy=True) != 0
+
+
+def run_calibration(
+    model_dir: str | os.PathLike[str],
+    rgb_dir: str | os.PathLike[str],
+    mask_dir: str | os.PathLike[str],
+    output_dir: str | os.PathLike[str],
+    *,
+    calibrator: Calibrator,
+) -> CalibrationOutputs:
+    dataset = load_dataset(model_dir, rgb_dir, mask_dir)
+    output_root = Path(output_dir)
+    resolved_output_root = output_root.resolve()
+    if resolved_output_root == Path(rgb_dir).resolve():
+        raise ValueError("output_dir must differ from rgb_dir")
+
+    result = calibrator(dataset)
+    if not isinstance(result, CalibrationResult):
+        raise ValueError("calibrator must return CalibrationResult")
+    if not isinstance(result.corrected_images, Mapping):
+        raise ValueError("calibrator corrected_images must be a mapping")
+    try:
+        report_json = json.dumps(
+            result.report,
+            ensure_ascii=False,
+            indent=2,
+            sort_keys=True,
+            allow_nan=False,
+        )
+    except (TypeError, ValueError) as error:
+        raise ValueError("calibration report is not valid JSON") from error
+
+    expected_ids = {view.image_id for view in dataset.views}
+    returned_ids = set(result.corrected_images)
+    if returned_ids != expected_ids:
+        missing = sorted(expected_ids - returned_ids)
+        extra = sorted(returned_ids - expected_ids, key=repr)
+        raise ValueError(
+            f"calibrator returned wrong image ids: missing={missing} extra={extra}"
+        )
+
+    Image.init()
+    output_formats = Image.registered_extensions()
+    seen_targets: set[str] = set()
+    seen_ancestors: set[str] = set()
+    outputs: list[tuple[Path, NDArray[np.uint8]]] = []
+    for view in dataset.views:
+        rgb = result.corrected_images[view.image_id]
+        expected_shape = (view.camera.height, view.camera.width, 3)
+        if (
+            not isinstance(rgb, np.ndarray)
+            or rgb.dtype != np.uint8
+            or rgb.shape != expected_shape
+        ):
+            raise ValueError(
+                f"invalid output for image {view.name}: expected shape "
+                f"{expected_shape} and dtype uint8, got shape "
+                f"{getattr(rgb, 'shape', None)} and dtype "
+                f"{getattr(rgb, 'dtype', None)}"
+            )
+        target = _validate_output_target(
+            output_root,
+            resolved_output_root,
+            _relative_image_path(view.name),
+            seen_targets,
+            seen_ancestors,
+        )
+        output_format = output_formats.get(target.suffix.lower())
+        if output_format not in Image.SAVE:
+            raise ValueError(f"unsupported output image extension: {target.suffix}")
+        outputs.append((target, rgb))
+
+    report_path = _validate_output_target(
+        output_root,
+        resolved_output_root,
+        Path("calibration_report.json"),
+        seen_targets,
+        seen_ancestors,
+    )
+    for target, rgb in outputs:
+        target.parent.mkdir(parents=True, exist_ok=True)
+        Image.fromarray(rgb).save(target)
+    report_path.parent.mkdir(parents=True, exist_ok=True)
+    report_path.write_text(f"{report_json}\n", encoding="utf-8")
+
+    return CalibrationOutputs(
+        image_paths=tuple(target for target, _ in outputs),
+        report_path=report_path,
+    )

color_change/calibration.py

@@ -0,0 +1,467 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass
+from itertools import combinations
+
+import numpy as np
+from numpy.typing import NDArray
+
+from calib_io import CalibrationDataset, CalibrationResult, read_mask, read_rgb
+
+
+_PATCH_RADIUS = 2
+
+
+@dataclass(frozen=True, slots=True)
+class CalibrationOptions:
+    mask_erosion_radius: int = 2
+    min_shared_tracks: int = 3
+    valid_color_min: int = 1
+    valid_color_max: int = 254
+    huber_k: float = 1.345
+    max_iterations: int = 20
+    tolerance: float = 1e-6
+
+
+def _validate_options(options: CalibrationOptions) -> None:
+    if options.mask_erosion_radius < 0:
+        raise ValueError("mask_erosion_radius must be non-negative")
+    if options.min_shared_tracks < 1:
+        raise ValueError("min_shared_tracks must be positive")
+    if not 1 <= options.valid_color_min < options.valid_color_max <= 255:
+        raise ValueError("valid color range must satisfy 1 <= min < max <= 255")
+    if not np.isfinite(options.huber_k) or options.huber_k <= 0:
+        raise ValueError("huber_k must be finite and positive")
+    if options.max_iterations < 1:
+        raise ValueError("max_iterations must be positive")
+    if not np.isfinite(options.tolerance) or options.tolerance <= 0:
+        raise ValueError("tolerance must be finite and positive")
+
+
+def _erode_mask(mask: NDArray[np.bool_], radius: int) -> NDArray[np.bool_]:
+    source = np.asarray(mask, dtype=np.bool_)
+    if radius == 0:
+        return source.copy()
+
+    result = np.zeros_like(source)
+    size = 2 * radius + 1
+    if source.shape[0] < size or source.shape[1] < size:
+        return result
+    windows = np.lib.stride_tricks.sliding_window_view(source, (size, size))
+    result[radius:-radius, radius:-radius] = windows.all(axis=(-2, -1))
+    return result
+
+
+def _sample_tracks(
+    dataset: CalibrationDataset,
+    options: CalibrationOptions,
+) -> tuple[dict[int, tuple[tuple[int, NDArray[np.float64]], ...]], int, int]:
+    observations: dict[int, list[tuple[int, NDArray[np.float64]]]] = defaultdict(
+        list
+    )
+    candidate_observations = 0
+    valid_observations = 0
+    duplicate_tracks: set[int] = set()
+
+    for view in dataset.views:
+        rgb = read_rgb(view)
+        eroded_mask = _erode_mask(
+            read_mask(view),
+            options.mask_erosion_radius,
+        )
+        # ✅ pycolmap 4.x: 从 images 字典取
+        image = dataset.reconstruction.images[view.image_id]
+        seen_point3d_ids: set[int] = set()
+        for point2D_idx in range(image.num_points2D()):
+            point = image.point2D(point2D_idx)
+            if not point.has_point3D():
+                continue
+            candidate_observations += 1
+            point3d_id = int(point.point3D_id)
+            if point3d_id in seen_point3d_ids:
+                duplicate_tracks.add(point3d_id)
+            else:
+                seen_point3d_ids.add(point3d_id)
+            x, y = np.asarray(point.xy, dtype=np.float64)
+            if not np.isfinite(x) or not np.isfinite(y):
+                continue
+            center_x = int(np.floor(x + 0.5))
+            center_y = int(np.floor(y + 0.5))
+            if not (
+                _PATCH_RADIUS <= center_x < rgb.shape[1] - _PATCH_RADIUS
+                and _PATCH_RADIUS <= center_y < rgb.shape[0] - _PATCH_RADIUS
+                and eroded_mask[center_y, center_x]
+            ):
+                continue
+            median = np.median(
+                rgb[
+                    center_y - _PATCH_RADIUS : center_y + _PATCH_RADIUS + 1,
+                    center_x - _PATCH_RADIUS : center_x + _PATCH_RADIUS + 1,
+                ].reshape(-1, 3),
+                axis=0,
+            )
+            if np.any(median < options.valid_color_min) or np.any(
+                median > options.valid_color_max
+            ):
+                continue
+            observations[point3d_id].append(
+                (view.image_id, median.astype(np.float64, copy=False))
+            )
+            valid_observations += 1
+        del rgb, eroded_mask, seen_point3d_ids
+
+    sampled: dict[int, tuple[tuple[int, NDArray[np.float64]], ...]] = {}
+    for point3d_id in sorted(observations):
+        if point3d_id in duplicate_tracks:
+            continue
+        track = sorted(observations[point3d_id], key=lambda item: item[0])
+        image_ids = [image_id for image_id, _ in track]
+        if len(track) < 2 or len(set(image_ids)) != len(image_ids):
+            continue
+        sampled[point3d_id] = tuple(track)
+    return sampled, candidate_observations, valid_observations
+
+
+def _build_constraints(
+    tracks: dict[int, tuple[tuple[int, NDArray[np.float64]], ...]],
+    min_shared_tracks: int,
+) -> tuple[
+    dict[tuple[int, int], int],
+    NDArray[np.int64],
+    NDArray[np.int64],
+    NDArray[np.float64],
+]:
+    edge_counts: dict[tuple[int, int], int] = defaultdict(int)
+    for point3d_id in sorted(tracks):
+        for (image_i, _), (image_j, _) in combinations(tracks[point3d_id], 2):
+            edge_counts[(image_i, image_j)] += 1
+
+    edges = {
+        edge: edge_counts[edge]
+        for edge in sorted(edge_counts)
+        if edge_counts[edge] >= min_shared_tracks
+    }
+    del edge_counts
+    constraint_count = sum(edges.values())
+    left = np.empty(constraint_count, dtype=np.int64)
+    right = np.empty(constraint_count, dtype=np.int64)
+    deltas = np.empty((constraint_count, 3), dtype=np.float64)
+    index = 0
+    for point3d_id in sorted(tracks):
+        for (image_i, color_i), (image_j, color_j) in combinations(
+            tracks[point3d_id], 2
+        ):
+            if (image_i, image_j) not in edges:
+                continue
+            left[index] = image_i
+            right[index] = image_j
+            deltas[index] = np.log(color_j / 255.0) - np.log(color_i / 255.0)
+            index += 1
+    return edges, left, right, deltas
+
+
+def _connected_components(
+    image_ids: tuple[int, ...],
+    edges: dict[tuple[int, int], int],
+) -> tuple[tuple[int, ...], ...]:
+    neighbors = {image_id: set() for image_id in image_ids}
+    for image_i, image_j in edges:
+        neighbors[image_i].add(image_j)
+        neighbors[image_j].add(image_i)
+
+    components: list[tuple[int, ...]] = []
+    remaining = set(image_ids)
+    while remaining:
+        pending = [min(remaining)]
+        component: set[int] = set()
+        while pending:
+            image_id = pending.pop()
+            if image_id in component:
+                continue
+            component.add(image_id)
+            pending.extend(sorted(neighbors[image_id] - component, reverse=True))
+        remaining -= component
+        components.append(tuple(sorted(component)))
+    return tuple(sorted(components, key=lambda component: component[0]))
+
+
+_ComponentSystem = tuple[
+    tuple[int, ...],
+    NDArray[np.int64],
+    NDArray[np.int64],
+    NDArray[np.int64],
+    NDArray[np.int64],
+]
+
+
+def _prepare_component_systems(
+    image_ids: tuple[int, ...],
+    components: tuple[tuple[int, ...], ...],
+    left_ids: NDArray[np.int64],
+    right_ids: NDArray[np.int64],
+) -> tuple[
+    NDArray[np.int64],
+    NDArray[np.int64],
+    tuple[_ComponentSystem, ...],
+]:
+    id_to_index = {image_id: index for index, image_id in enumerate(image_ids)}
+    left_indices = np.fromiter(
+        (id_to_index[int(value)] for value in left_ids),
+        dtype=np.int64,
+        count=len(left_ids),
+    )
+    right_indices = np.fromiter(
+        (id_to_index[int(value)] for value in right_ids),
+        dtype=np.int64,
+        count=len(right_ids),
+    )
+    component_by_index = np.empty(len(image_ids), dtype=np.int64)
+    component_indices: list[NDArray[np.int64]] = []
+    for component_index, component in enumerate(components):
+        indices = np.fromiter(
+            (id_to_index[image_id] for image_id in component),
+            dtype=np.int64,
+            count=len(component),
+        )
+        component_indices.append(indices)
+        component_by_index[indices] = component_index
+
+    if len(components) == 1:
+        rows_by_component = (np.arange(len(left_indices), dtype=np.int64),)
+    else:
+        constraint_components = component_by_index[left_indices]
+        order = np.argsort(constraint_components, kind="stable")
+        offsets = np.concatenate(
+            (
+                np.zeros(1, dtype=np.int64),
+                np.cumsum(
+                    np.bincount(
+                        constraint_components,
+                        minlength=len(components),
+                    )
+                ),
+            )
+        )
+        rows_by_component = tuple(
+            order[offsets[index] : offsets[index + 1]]
+            for index in range(len(components))
+        )
+
+    systems: list[_ComponentSystem] = []
+    global_to_local = np.empty(len(image_ids), dtype=np.int64)
+    for component_index, component in enumerate(components):
+        indices = component_indices[component_index]
+        global_to_local[indices] = np.arange(len(indices), dtype=np.int64)
+        rows = rows_by_component[component_index]
+        systems.append(
+            (
+                component,
+                indices,
+                rows,
+                global_to_local[left_indices[rows]].copy(),
+                global_to_local[right_indices[rows]].copy(),
+            )
+        )
+    return left_indices, right_indices, tuple(systems)
+
+
+def _solve_weighted_channel(
+    image_count: int,
+    systems: tuple[_ComponentSystem, ...],
+    deltas: NDArray[np.float64],
+    weights: NDArray[np.float64],
+) -> NDArray[np.float64]:
+    solved = np.zeros(image_count, dtype=np.float64)
+    for component, indices, rows, local_left, local_right in systems:
+        if len(component) == 1:
+            continue
+        row_weights = weights[rows]
+        row_deltas = deltas[rows]
+        size = len(component)
+        laplacian = np.zeros((size, size), dtype=np.float64)
+        rhs = np.zeros(size, dtype=np.float64)
+        np.add.at(laplacian, (local_left, local_left), row_weights)
+        np.add.at(laplacian, (local_right, local_right), row_weights)
+        np.add.at(laplacian, (local_left, local_right), -row_weights)
+        np.add.at(laplacian, (local_right, local_left), -row_weights)
+        np.add.at(rhs, local_left, row_weights * row_deltas)
+        np.add.at(rhs, local_right, -row_weights * row_deltas)
+        component_solution = np.zeros(size, dtype=np.float64)
+        try:
+            component_solution[1:] = np.linalg.solve(
+                laplacian[1:, 1:], rhs[1:]
+            )
+        except np.linalg.LinAlgError as error:
+            raise ValueError(
+                f"gain solve failed for component {list(component)}"
+            ) from error
+        component_solution -= component_solution.mean()
+        solved[indices] = component_solution
+    return solved
+
+
+def _solve_log_gains(
+    image_ids: tuple[int, ...],
+    components: tuple[tuple[int, ...], ...],
+    left_ids: NDArray[np.int64],
+    right_ids: NDArray[np.int64],
+    delta_log_rgb: NDArray[np.float64],
+    options: CalibrationOptions,
+) -> tuple[NDArray[np.float64], list[int], list[bool], list[float]]:
+    log_gains = np.zeros((len(image_ids), 3), dtype=np.float64)
+    if len(left_ids) == 0:
+        return log_gains, [0, 0, 0], [True, True, True], [0.0, 0.0, 0.0]
+
+    left_indices, right_indices, systems = _prepare_component_systems(
+        image_ids,
+        components,
+        left_ids,
+        right_ids,
+    )
+    iterations_rgb: list[int] = []
+    converged_rgb: list[bool] = []
+    residual_rgb: list[float] = []
+
+    for channel in range(3):
+        weights = np.ones(len(left_indices), dtype=np.float64)
+        previous: NDArray[np.float64] | None = None
+        converged = False
+        solution = np.zeros(len(image_ids), dtype=np.float64)
+        iterations = 0
+        for iterations in range(1, options.max_iterations + 1):
+            solution = _solve_weighted_channel(
+                len(image_ids),
+                systems,
+                delta_log_rgb[:, channel],
+                weights,
+            )
+            if previous is not None and np.max(np.abs(solution - previous)) <= options.tolerance:
+                converged = True
+                break
+            residual = (
+                solution[left_indices]
+                - solution[right_indices]
+                - delta_log_rgb[:, channel]
+            )
+            scale = max(1.4826 * float(np.median(np.abs(residual))), 1e-6)
+            threshold = options.huber_k * scale
+            absolute_residual = np.abs(residual)
+            weights = np.where(
+                absolute_residual <= threshold,
+                1.0,
+                threshold / np.maximum(absolute_residual, np.finfo(np.float64).tiny),
+            )
+            previous = solution
+
+        final_residual = (
+            solution[left_indices]
+            - solution[right_indices]
+            - delta_log_rgb[:, channel]
+        )
+        log_gains[:, channel] = solution
+        iterations_rgb.append(iterations)
+        converged_rgb.append(converged)
+        residual_rgb.append(float(np.median(np.abs(final_residual))))
+
+    if not np.all(np.isfinite(log_gains)):
+        raise ValueError("gain solve produced non-finite values")
+    return log_gains, iterations_rgb, converged_rgb, residual_rgb
+
+
+def calibrate(
+    dataset: CalibrationDataset,
+    *,
+    options: CalibrationOptions = CalibrationOptions(),
+) -> CalibrationResult:
+    _validate_options(options)
+    tracks, candidate_observations, valid_observations = _sample_tracks(
+        dataset, options
+    )
+    edges, left_ids, right_ids, delta_log_rgb = _build_constraints(
+        tracks, options.min_shared_tracks
+    )
+    constraint_count = len(left_ids)
+    sampled_track_count = len(tracks)
+    del tracks
+    image_ids = tuple(sorted(view.image_id for view in dataset.views))
+    components = _connected_components(image_ids, edges)
+    log_gains, iterations, converged, residuals = _solve_log_gains(
+        image_ids,
+        components,
+        left_ids,
+        right_ids,
+        delta_log_rgb,
+        options,
+    )
+    del left_ids, right_ids, delta_log_rgb
+    gains = np.exp(log_gains)
+    if not np.all(np.isfinite(gains)):
+        raise ValueError("gain solve produced non-finite values")
+
+    id_to_index = {image_id: index for index, image_id in enumerate(image_ids)}
+    corrected_images: dict[int, NDArray[np.uint8]] = {}
+    for view in dataset.views:
+        corrected = read_rgb(view).astype(np.float32)
+        corrected *= gains[id_to_index[view.image_id]].astype(np.float32)
+        np.rint(corrected, out=corrected)
+        np.clip(corrected, 0, 255, out=corrected)
+        corrected_images[view.image_id] = corrected.astype(np.uint8)
+        del corrected
+
+    component_by_image = {
+        image_id: component_index
+        for component_index, component in enumerate(components)
+        for image_id in component
+    }
+    report = {
+        "schema_version": 1,
+        "settings": {
+            "patch_size": 2 * _PATCH_RADIUS + 1,
+            "mask_erosion_radius": options.mask_erosion_radius,
+            "min_shared_tracks": options.min_shared_tracks,
+            "valid_color_range": [
+                options.valid_color_min,
+                options.valid_color_max,
+            ],
+            "huber_k": options.huber_k,
+            "max_iterations": options.max_iterations,
+            "tolerance": options.tolerance,
+        },
+        "summary": {
+            "images": len(dataset.views),
+            "total_tracks": dataset.reconstruction.num_points3D(),
+            "candidate_observations": candidate_observations,
+            "valid_observations": valid_observations,
+            "sampled_tracks": sampled_track_count,
+            "retained_edges": len(edges),
+            "constraints": constraint_count,
+            "connected_components": len(components),
+        },
+        "images": [
+            {
+                "image_id": view.image_id,
+                "name": view.name,
+                "component": component_by_image[view.image_id],
+                "log_gain_rgb": log_gains[
+                    id_to_index[view.image_id]
+                ].tolist(),
+                "gain_rgb": gains[id_to_index[view.image_id]].tolist(),
+            }
+            for view in dataset.views
+        ],
+        "edges": [
+            {
+                "image_id_a": image_i,
+                "image_id_b": image_j,
+                "shared_tracks": shared_tracks,
+            }
+            for (image_i, image_j), shared_tracks in edges.items()
+        ],
+        "solver": {
+            "iterations_rgb": iterations,
+            "converged_rgb": converged,
+            "median_abs_residual_rgb": residuals,
+        },
+    }
+    return CalibrationResult(corrected_images=corrected_images, report=report)

color_change/convert_txt_to_bin.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""把 COLMAP txt 格式转成 bin 格式（用 pycolmap 4.x 写入）"""
+import sys
+import struct
+import numpy as np
+from pathlib import Path
+
+
+def write_cameras_bin(cameras, output_path):
+    """写 cameras.bin"""
+    with open(output_path, 'wb') as f:
+        f.write(struct.pack('Q', len(cameras)))  # uint64 num_cameras
+        for cam_id in sorted(cameras.keys()):
+            cam = cameras[cam_id]
+            f.write(struct.pack('I', cam_id))  # uint32 camera_id
+            # model 映射：SIMPLE_PINHOLE=0, PINHOLE=1, SIMPLE_RADIAL=2, etc.
+            model_map = {
+                'SIMPLE_PINHOLE': 0,
+                'PINHOLE': 1,
+                'SIMPLE_RADIAL': 2,
+                'RADIAL': 3,
+                'OPENCV': 4,
+                'OPENCV_FISHEYE': 5,
+                'FULL_OPENCV': 6,
+                'FOV': 7,
+                'THIN_PRISM_FISHEYE': 8,
+            }
+            model_id = model_map.get(cam['model'], 0)
+            f.write(struct.pack('I', model_id))  # uint32 model_id
+            f.write(struct.pack('Q', cam['width']))  # uint64 width
+            f.write(struct.pack('Q', cam['height']))  # uint64 height
+            # params
+            for p in cam['params']:
+                f.write(struct.pack('d', float(p)))
+
+
+def write_images_bin(images, output_path):
+    """写 images.bin"""
+    with open(output_path, 'wb') as f:
+        f.write(struct.pack('Q', len(images)))  # uint64 num_images
+        for img_id in sorted(images.keys()):
+            img = images[img_id]
+            f.write(struct.pack('I', img_id))  # uint32 image_id
+            # qvec (4 doubles)
+            for q in img['q']:
+                f.write(struct.pack('d', float(q)))
+            # tvec (3 doubles)
+            for t in img['t']:
+                f.write(struct.pack('d', float(t)))
+            f.write(struct.pack('I', img['cam_id']))  # uint32 camera_id
+            # name (null-terminated string)
+            name_bytes = img['name'].encode('utf-8')
+            f.write(name_bytes)
+            f.write(b'\x00')
+            # num_points2D (uint64)
+            f.write(struct.pack('Q', 0))  # 简化：0 个 2D 点
+
+
+def write_points3D_bin(points3D, output_path):
+    """写 points3D.bin"""
+    with open(output_path, 'wb') as f:
+        f.write(struct.pack('Q', len(points3D)))  # uint64 num_points3D
+        for pt_id in sorted(points3D.keys()):
+            pt = points3D[pt_id]
+            f.write(struct.pack('Q', pt_id))  # uint64 point3D_id
+            # xyz (3 doubles)
+            for v in pt['xyz']:
+                f.write(struct.pack('d', float(v)))
+            # rgb (3 uint8)
+            f.write(struct.pack('B', 0))
+            f.write(struct.pack('B', 0))
+            f.write(struct.pack('B', 0))
+            # error (double)
+            f.write(struct.pack('d', 0.0))
+            # track_length (uint64)
+            f.write(struct.pack('Q', 0))  # 简化：空 track
+
+
+def main():
+    input_dir = Path(sys.argv[1])
+    output_dir = Path(sys.argv[2])
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Reading txt from {input_dir}")
+
+    # 解析 cameras.txt
+    cameras = {}
+    with open(input_dir / "cameras.txt") as f:
+        for line in f:
+            if line.startswith('#'):
+                continue
+            parts = line.strip().split()
+            if len(parts) < 5:
+                continue
+            cam_id = int(parts[0])
+            cameras[cam_id] = {
+                'model': parts[1],
+                'width': int(parts[2]),
+                'height': int(parts[3]),
+                'params': [float(x) for x in parts[4:]],
+            }
+
+    # 解析 images.txt
+    images = {}
+    with open(input_dir / "images.txt") as f:
+        lines = f.readlines()
+    i = 0
+    while i < len(lines):
+        line = lines[i].strip()
+        i += 1
+        if not line or line.startswith('#'):
+            continue
+        parts = line.split()
+        if len(parts) < 10:
+            continue
+        img_id = int(parts[0])
+        q = [float(x) for x in parts[1:5]]
+        t = [float(x) for x in parts[5:8]]
+        cam_id = int(parts[8])
+        name = parts[9]
+        images[img_id] = {'name': name, 'cam_id': cam_id, 'q': q, 't': t}
+        # 跳过 2D 点行
+        if i < len(lines):
+            i += 1
+
+    # 解析 points3D.txt
+    points3D = {}
+    with open(input_dir / "points3D.txt") as f:
+        for line in f:
+            if line.startswith('#'):
+                continue
+            parts = line.strip().split()
+            if len(parts) < 5:
+                continue
+            pt_id = int(parts[0])
+            xyz = [float(x) for x in parts[1:4]]
+            points3D[pt_id] = {'xyz': xyz}
+
+    print(f"Found: {len(cameras)} cameras, {len(images)} images, {len(points3D)} points")
+
+    # 写 bin
+    write_cameras_bin(cameras, output_dir / "cameras.bin")
+    write_images_bin(images, output_dir / "images.bin")
+    write_points3D_bin(points3D, output_dir / "points3D.bin")
+
+    print(f"Written bin files to {output_dir}")
+    print(f"Files: cameras.bin, images.bin, points3D.bin")
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 3:
+        print("Usage: python convert_txt_to_bin.py <input_txt_dir> <output_bin_dir>")
+        sys.exit(1)
+    main()

color_change/run_calibration.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+"""光度标定入口脚本
+
+用法:
+    python run_calibration.py \
+        --model_dir /path/to/colmap_model \
+        --rgb_dir /path/to/rgb \
+        --mask_dir /path/to/masks \
+        --output_dir /path/to/output
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+from calib_io import run_calibration
+from calibration import calibrate, CalibrationOptions
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Photometric calibration using COLMAP reconstruction",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--model_dir", "-m",
+        type=str, required=True,
+        help="COLMAP sparse reconstruction directory (contains cameras.bin/images.bin/points3D.bin)",
+    )
+    parser.add_argument(
+        "--rgb_dir", "-r",
+        type=str, required=True,
+        help="Directory containing input RGB images",
+    )
+    parser.add_argument(
+        "--mask_dir", "-mk",
+        type=str, required=True,
+        help="Directory containing foreground masks (.png, same names as RGB)",
+    )
+    parser.add_argument(
+        "--output_dir", "-o",
+        type=str, required=True,
+        help="Output directory for corrected images and report",
+    )
+
+    # ---- 可选参数 ----
+    parser.add_argument("--mask_erosion_radius", type=int, default=2)
+    parser.add_argument("--min_shared_tracks", type=int, default=3)
+    parser.add_argument("--valid_color_min", type=int, default=1)
+    parser.add_argument("--valid_color_max", type=int, default=254)
+    parser.add_argument("--huber_k", type=float, default=1.345)
+    parser.add_argument("--max_iterations", type=int, default=20)
+    parser.add_argument("--tolerance", type=float, default=1e-6)
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # 路径校验
+    model_dir = Path(args.model_dir)
+    rgb_dir = Path(args.rgb_dir)
+    mask_dir = Path(args.mask_dir)
+    output_dir = Path(args.output_dir)
+
+    if not model_dir.exists():
+        print(f"[ERROR] model_dir does not exist: {model_dir}", file=sys.stderr)
+        sys.exit(1)
+    if not rgb_dir.exists():
+        print(f"[ERROR] rgb_dir does not exist: {rgb_dir}", file=sys.stderr)
+        sys.exit(1)
+    if not mask_dir.exists():
+        print(f"[ERROR] mask_dir does not exist: {mask_dir}", file=sys.stderr)
+        sys.exit(1)
+
+    # 参数
+    options = CalibrationOptions(
+        mask_erosion_radius=args.mask_erosion_radius,
+        min_shared_tracks=args.min_shared_tracks,
+        valid_color_min=args.valid_color_min,
+        valid_color_max=args.valid_color_max,
+        huber_k=args.huber_k,
+        max_iterations=args.max_iterations,
+        tolerance=args.tolerance,
+    )
+
+    print("=" * 50)
+    print("Photometric Calibration")
+    print("=" * 50)
+    print(f"  model_dir     : {model_dir}")
+    print(f"  rgb_dir       : {rgb_dir}")
+    print(f"  mask_dir      : {mask_dir}")
+    print(f"  output_dir    : {output_dir}")
+    print(f"  min_shared_tracks: {options.min_shared_tracks}")
+    print(f"  mask_erosion_radius: {options.mask_erosion_radius}")
+    print("-" * 50)
+
+    # 执行标定
+    print("Starting calibration...")
+    print(f"model_dir={model_dir}")
+    outputs = run_calibration(
+        model_dir=str(model_dir),
+        rgb_dir=str(rgb_dir),
+        mask_dir=str(mask_dir),
+        output_dir=str(output_dir),
+        calibrator=lambda dataset: calibrate(dataset, options=options),
+    )
+
+    print("=" * 50)
+    print(f"[DONE] Corrected images: {len(outputs.image_paths)}")
+    print(f"        Output dir    : {output_dir}")
+    print(f"        Report        : {outputs.report_path}")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    main()

fill2sc/make_shell.py

@@ -0,0 +1,482 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+make_shell.py — 为每个眼镜实例的顶点连通区域生成"最小壳" (minimal tight shell)。
+
+输入支持两种方式:
+  1. data.json (推荐, 支持多眼镜实例, 每个实例单独处理并各导出一个壳):
+       {"input": "xxx.obj",
+        "instances": [
+          {"id": 0, "type": "ring-pair", "score": 0.91, "count": 1234,
+           "indices": [12, 13, 14, ...]},
+          {"id": 1, "type": "ring-pair", "score": 0.87, "count": 1180,
+           "indices": [5012, 5013, ...]}
+        ]}
+     "input" 相对 data.json 所在目录解析 (解析不到时按同名 obj 兜底); 也可用 --obj 显式指定 (优先级更高)。
+     各实例独立计算, 壳最后合并导出为单个 obj (默认 shell.obj)。
+  2. index.txt (兼容旧的单实例流程): 顶点索引, 每行一个, 输出单个 shell.obj。
+
+不是 AABB, 也不是带 padding 的膨胀包裹, 而是 alpha shape (3D 凹包):
+  - 壳面由区域最外侧的原始采样点构成, 紧贴外侧轮廓 (含凹陷, 如眼镜框);
+  - alpha 自动取"使壳保持单一连通的最小值" (二分搜索), 即最小壳;
+  - 桥接只发生在顶点漏采的空洞处, 桥的粗细由最小 alpha 自然决定;
+  - 最后做可选的 Taubin 平滑 (不收缩), 兼顾"最小"与"光滑"。
+
+流程 (每个实例独立执行一遍):
+  1. 读 obj (全局一次) 与实例顶点索引 (默认 0-based), 取区域顶点 +
+     "三顶点均在区域内"的三角形重心/边中点加密 -> 采样点;
+  2. 3D Delaunay 四面体化, 计算每个四面体的外接球半径;
+  3. 二分搜索最小 alpha: alpha 复合体 (外接球半径 <= alpha 的四面体之并)
+     满足 (a) 连通 (b) 覆盖全部采样点;
+  4. 若最小 alpha 下边界非流形/不水密, alpha 向上微调至水密;
+  5. 抽复合体的边界三角形 (外向绕序), 得到紧贴的壳;
+  6. 可选 Taubin 平滑 + 数值校验 (点偏移, 体积, 水密性);
+  7. 全部实例的壳合并 (同一坐标系直接拼接) 导出单个 obj。
+
+用法:
+  python make_shell.py                          # 读同目录 data.json, 各实例合并导出 shell.obj
+  python make_shell.py --data other.json        # 指定 data.json
+  python make_shell.py --index index.txt        # 兼容旧单索引, 导出 shell.obj
+  python make_shell.py --alpha 3.0              # 手动指定 alpha (更大=更圆润)
+  python make_shell.py --smooth 0               # 关闭平滑, 严格贴点
+"""
+import argparse
+import glob
+import json
+import os
+
+import numpy as np
+from scipy.spatial import Delaunay, cKDTree
+from scipy.sparse import coo_matrix
+from scipy.sparse.csgraph import connected_components
+from scipy import ndimage
+from skimage import measure
+import trimesh
+from trimesh.smoothing import filter_taubin
+
+
+# ---------------------------------------------------------------- IO
+def load_obj(path):
+    verts, faces = [], []
+    with open(path) as f:
+        for line in f:
+            if line.startswith("v "):
+                verts.append([float(x) for x in line.split()[1:4]])
+            elif line.startswith("f "):
+                faces.append([int(t.split("/")[0]) - 1 for t in line.split()[1:4]])
+    return np.asarray(verts, np.float64), np.asarray(faces, np.int64)
+
+
+def load_data(args):
+    """解析输入, 返回 (instances, input_obj)。
+
+    instances: [(实例名, 顶点索引 ndarray), ...]
+    input_obj: data.json 声明的 obj 路径 (未解析相对路径, 可能为 None)
+
+    优先级: --index 单实例 txt > --data 多实例 json。
+    """
+    if args.index:
+        idx = np.loadtxt(args.index, dtype=np.int64).ravel()
+        if args.one_based:
+            idx = idx - 1
+        return [("index", idx)], None
+
+    if not os.path.exists(args.data):
+        raise SystemExit(f"找不到 {args.data}, 请用 --data 指定 data.json, "
+                         f"或用 --index 指定单实例索引文件")
+    with open(args.data) as f:
+        data = json.load(f)
+
+    insts = []
+    for inst in data.get("instances", []):
+        name = str(inst.get("id", len(insts)))
+        idx = np.asarray(inst.get("indices", []), dtype=np.int64).ravel()
+        if args.one_based:
+            idx = idx - 1
+        cnt = inst.get("count")
+        if cnt is not None and cnt != len(idx):
+            print(f"[warn] 实例 {name}: count={cnt} 与 indices 数量 {len(idx)} 不一致")
+        if len(idx) == 0:
+            print(f"[warn] 实例 {name}: 索引为空, 跳过")
+            continue
+        insts.append((name, idx))
+    if not insts:
+        raise SystemExit(f"{args.data} 中没有可用实例 (instances 为空)")
+    return insts, data.get("input")
+
+
+def resolve_obj(args, input_obj):
+    """确定 obj 路径: --obj 显式指定 > data.json 的 input 字段 > data 目录内唯一 obj。
+
+    input 字段可能是相对其他根目录的路径 (如 "datasets/xxx/xxx.obj"), 本地解析不到时
+    按 basename 在 data 目录内找同名 obj 兜底。
+    """
+    if args.obj:
+        return args.obj
+    base = os.path.dirname(os.path.abspath(args.data))
+    if input_obj:
+        p = input_obj if os.path.isabs(input_obj) else os.path.join(base, input_obj)
+        if os.path.exists(p):
+            return p
+        name = os.path.basename(input_obj)     # 按文件名在 data 目录内找同名 obj
+        cands = sorted(glob.glob(os.path.join(base, name)))
+        if len(cands) == 1:
+            print(f"[warn] data.json 的 input '{input_obj}' 本地不存在, "
+                  f"使用同名 obj: {cands[0]}")
+            return cands[0]
+        print(f"[warn] data.json 的 input '{input_obj}' 本地不存在, 尝试目录内查找 obj")
+    cands = sorted(glob.glob(os.path.join(base, "*.obj")))
+    if len(cands) == 1:
+        return cands[0]
+    if not cands:
+        raise SystemExit(f"未找到 obj: 请用 --obj 指定, 或在 {base} 放置 .obj 文件")
+    raise SystemExit(f"{base} 下有多个 obj, 请用 --obj 指定其一: "
+                     f"{', '.join(os.path.basename(c) for c in cands)}")
+
+
+def densify_points(verts, faces, region_mask):
+    """区域顶点 + 区域内部三角形的重心/边中点采样点。"""
+    pts = [verts[region_mask]]
+    inner = faces[np.all(region_mask[faces], axis=1)]
+    if len(inner):
+        tris = verts[inner]                                   # (F,3,3)
+        for a, b, c in ((1/3, 1/3, 1/3),
+                        (0.5, 0.5, 0.0), (0.5, 0.0, 0.5), (0.0, 0.5, 0.5)):
+            pts.append(a * tris[:, 0] + b * tris[:, 1] + c * tris[:, 2])
+    pts = np.concatenate(pts, axis=0)
+    return np.unique(pts, axis=0), inner                      # 去重, 避免退化四面体
+
+
+def largest_cluster(pts, r):
+    """半径 r 欧氏聚类, 返回最大簇的布尔掩码 (剔除分割噪声)。"""
+    tree = cKDTree(pts)
+    pairs = np.array(list(tree.query_pairs(r=r)), dtype=np.int64).reshape(-1, 2)
+    n = len(pts)
+    if len(pairs):
+        m = coo_matrix((np.ones(len(pairs)), (pairs[:, 0], pairs[:, 1])), shape=(n, n))
+        _, lab = connected_components(m, directed=False)
+        sizes = np.bincount(lab)
+        return lab == int(np.argmax(sizes)), int(sizes.max())
+    return np.ones(n, bool), n
+
+
+# ------------------------------------------------------ Delaunay 预处理
+class AlphaStructure:
+    """Delaunay 四面体化 + 面邻接/外接球半径, 供 alpha 二分搜索复用。"""
+
+    def __init__(self, pts):
+        self.pts = pts
+        tri = Delaunay(pts)
+        self.tri = tri                                      # find_simplex 用于凸包内部栅格化
+        tets = tri.simplices.copy()
+        a = pts[tets[:, 0]]
+        m = np.stack([pts[tets[:, 1]] - a, pts[tets[:, 2]] - a, pts[tets[:, 3]] - a], axis=1)
+        det = np.linalg.det(m)
+        scale = float(np.abs(det).max()) ** (1 / 3) + 1e-30
+        good = np.abs(det) > 1e-10 * scale                    # 剔除退化四面体
+        tets, det, a = tets[good], det[good], a[good]
+        tets[det < 0] = tets[det < 0][:, [0, 1, 3, 2]]        # 统一正体积绕序
+        self.tets = tets
+        self.T = len(tets)
+
+        # 外接球半径: 解 2(b-a).x = |b|^2-|a|^2 线性方程组
+        e = [np.einsum("ij,ij->i", pts[tets[:, k]], pts[tets[:, k]]) for k in range(4)]
+        A = 2.0 * np.stack([pts[tets[:, 1]] - a, pts[tets[:, 2]] - a, pts[tets[:, 3]] - a], axis=1)
+        rhs = np.stack([e[1] - e[0], e[2] - e[0], e[3] - e[0]], axis=1)
+        center = np.linalg.solve(A, rhs)
+        self.circum = np.linalg.norm(center - a, axis=1)
+
+        # 每个四面体的 4 个面 (tet 优先排列: 线性位置 t*4+f), 外向绕序 + 面共享关系
+        faces = np.concatenate([tets[:, [1, 2, 3]], tets[:, [0, 3, 2]],
+                                tets[:, [0, 1, 3]], tets[:, [0, 2, 1]]],
+                               axis=1).reshape(self.T * 4, 3)
+        fcent = pts[faces].mean(axis=1)
+        tcent = np.repeat(pts[tets].mean(axis=1), 4, axis=0)
+        n = np.cross(pts[faces[:, 1]] - pts[faces[:, 0]], pts[faces[:, 2]] - pts[faces[:, 0]])
+        flip = np.einsum("ij,ij->i", n, fcent - tcent) < 0
+        faces[flip] = faces[flip][:, [0, 2, 1]]                # 法向朝外
+        self.faces = faces
+
+        keys = np.sort(self.faces, axis=1)
+        _, inv = np.unique(keys, axis=0, return_inverse=True)
+        self.face_id = np.asarray(inv).reshape(-1)
+        cnt = np.bincount(self.face_id)
+        shared = np.flatnonzero(cnt == 2)
+        # 每个共享面 -> 一对四面体 (面在线性数组中的两个出现位置)
+        order = np.argsort(self.face_id, kind="stable")
+        inv_sorted = self.face_id[order]
+        starts = np.searchsorted(inv_sorted, shared, side="left")
+        self.pairs = np.stack([order[starts], order[starts + 1]], axis=1) // 4
+        self.n_pts = len(pts)
+
+    # ---- 给定 alpha 的复合体状态 --------------------------------------
+    def status(self, alpha):
+        keep = self.circum <= alpha
+        used = np.zeros(self.n_pts, bool)
+        used[self.tets[keep].ravel()] = True
+        ok_used = bool(used.all())
+        pe = keep[self.pairs[:, 0]] & keep[self.pairs[:, 1]]
+        g = coo_matrix((np.ones(int(pe.sum()), bool),
+                        (self.pairs[pe, 0], self.pairs[pe, 1])), shape=(self.T, self.T))
+        nc, lab = connected_components(g, directed=False)
+        sizes = np.bincount(lab, weights=keep.astype(np.int64))
+        ok_conn = nc - int((~keep).sum()) == 1 and int(sizes.max()) == int(keep.sum())
+        return keep, ok_used, ok_conn
+
+    def boundary_faces(self, keep):
+        """复合体的边界三角形 (恰属于一个保留四面体的面, 外向绕序)。"""
+        mask = np.repeat(keep, 4)                             # faces 按 [tet 的 4 面] 排列
+        fid = self.face_id[mask]
+        cnt = np.bincount(fid)                                # 该面在保留四面体中的出现次数
+        return self.faces[mask][cnt[fid] == 1]
+
+
+# ------------------------------------------------------ 眼镜盒式外壳
+def build_case(pts, tri, res, sigma, faces_target):
+    """凸包(最外侧点连成的最小凸外壳) -> 内部栅格化 -> 高斯平滑 -> MC。
+
+    壳贴着最外侧点(误差 ~ 1 voxel), 边角以 sigma*voxel 尺度圆润, 均匀光滑, 水密。
+    """
+    hull = trimesh.Trimesh(pts).convex_hull
+    lo, hi = pts.min(axis=0), pts.max(axis=0)
+    voxel = (hi - lo).max() / res
+    pad = 6 * sigma * voxel + 0.5
+    lo2 = lo - pad
+    dims = np.maximum(np.ceil((hi + pad - lo2) / voxel).astype(int) + 1, 2)
+
+    # 体素中心是否在凸包内 (Delaunay find_simplex, C 实现)
+    grids = np.meshgrid(*[lo2[i] + (np.arange(dims[i]) + 0.5) * voxel for i in range(3)], indexing="ij")
+    centers = np.stack([g.ravel() for g in grids], axis=1)
+    occ = (tri.find_simplex(centers) >= 0).reshape(dims)   # 体素中心在凸包内
+
+    field = ndimage.gaussian_filter(occ.astype(np.float32), sigma=sigma)
+    v, f, _, _ = measure.marching_cubes(field, level=0.5)
+    v = (v + 0.5) * voxel + lo2                            # 网格索引 -> 世界坐标
+    m = trimesh.Trimesh(vertices=v, faces=f[:, ::-1], process=True)
+    m = max(m.split(only_watertight=False), key=lambda x: x.volume)
+    filter_taubin(m, lamb=0.5, nu=0.5, iterations=5)
+
+    if faces_target and len(m.faces) > faces_target:       # pymeshlab 简化 + 轻平滑
+        import pymeshlab as pml
+        ms = pml.MeshSet()
+        ms.add_mesh(pml.Mesh(vertex_matrix=m.vertices, face_matrix=m.faces.astype(np.int64)))
+        ms.meshing_decimation_quadric_edge_collapse(targetfacenum=int(faces_target),
+                                                    preservenormal=True)
+        out = ms.current_mesh()
+        m = trimesh.Trimesh(vertices=np.array(out.vertex_matrix()),
+                            faces=np.array(out.face_matrix()), process=False)
+        filter_taubin(m, lamb=0.5, nu=0.5, iterations=3)
+    return m, hull
+
+
+# ------------------------------------------------------ 单实例流水线
+def run_instance(verts, faces, idx, args):
+    """处理一个眼镜实例 (一组顶点索引) 的完整流程, 返回该实例的壳 mesh (由调用方合并导出)。"""
+    assert idx.min() >= 0 and idx.max() < len(verts), \
+        "index 超出顶点范围, 请确认索引含义 (0/1-based, --one-based 可切换)"
+    region_mask = np.zeros(len(verts), bool)
+    region_mask[idx] = True                                     # Blender 0-based 顶点索引
+
+    # ---- 噪声过滤: 只保留最大空间簇 (index 中混有 SAM3 多视角假阳性) ----
+    if args.filter == "main":
+        ridx = np.flatnonzero(region_mask)
+        mkeep, nkeep = largest_cluster(verts[ridx], args.cluster_r)
+        region_mask[ridx[~mkeep]] = False
+        print(f"[filter] 聚类(r={args.cluster_r}) 保留最大簇 {nkeep}/{len(ridx)} 顶点, "
+              f"剔除 {len(ridx) - nkeep} 个噪声点")
+
+    pts, inner = densify_points(verts, faces, region_mask)
+    print(f"[region] 顶点 {int(region_mask.sum())}, 内部面 {len(inner)}, "
+          f"采样点 {len(pts)}, bbox {np.round(pts.max(0) - pts.min(0), 3)}")
+
+    print("[delaunay] 四面体化中 ...")
+    st = AlphaStructure(pts)
+    print(f"[delaunay] 四面体 {st.T}, 采样密度决定的最小可行 alpha ~ "
+          f"{np.percentile(st.circum, 50):.3f}")
+
+    if args.mode == "case":
+        # ---- 眼镜盒模式: 最外侧点连成凸外壳 + 栅格化高斯隐式面 + MC -----
+        mesh, hull = build_case(pts, st.tri, args.res, args.sigma, args.case_faces)
+        kd = cKDTree(mesh.vertices)
+        dd, _ = kd.query(pts, k=1)
+        print(f"[case] 凸包体积 {hull.volume:.1f} -> 壳体积 {mesh.volume:.1f} "
+              f"({mesh.volume / hull.volume * 100:.1f}%)")
+        print(f"[shell] 顶点 {len(mesh.vertices)}, 面 {len(mesh.faces)}, "
+              f"水密 {mesh.is_watertight}, 欧拉数 {mesh.euler_number}, "
+              f"bbox {np.round(mesh.extents, 3)} (区域 bbox {np.round(pts.max(0) - pts.min(0), 3)})")
+        print(f"[fit] 采样点到壳最近距离: mean={dd.mean():.4f}, "
+              f"p99={np.percentile(dd, 99):.4f}, max={dd.max():.4f}")
+        return mesh
+
+    # ---- 最小 alpha 二分搜索 ------------------------------------------
+    if args.alpha is not None:
+        alpha = args.alpha
+        keep, ok_used, ok_conn = st.status(alpha)
+        print(f"[alpha] 手动 alpha={alpha:.4f}, 连通={ok_conn}, 覆盖={ok_used}")
+    else:
+        cand = np.unique(st.circum)
+        lo, hi = 0, len(cand) - 1
+        keep, ok_used, ok_conn = st.status(cand[hi])
+        assert ok_conn and ok_used, "最大 alpha 仍不连通, 数据异常"
+        while lo < hi:
+            mid = (lo + hi) // 2
+            k, u, c = st.status(cand[mid])
+            if u and c:
+                hi, keep, ok_used, ok_conn = mid, k, u, c
+            else:
+                lo = mid + 1
+        alpha = float(cand[lo])
+        print(f"[alpha] 最小连通 alpha = {alpha:.4f}")
+
+    # ---- 边界抽取 + 掐点拓扑修复 (仅局部补四面体, 不牺牲最小性) ---------
+    def try_extract(keep):
+        """抽边界; 若有非 2 重边(掐点), 把该边周围的四面体扇补进复合体再试。"""
+        keep = keep.copy()
+        for _ in range(12):
+            bf = st.boundary_faces(keep)
+            m = trimesh.Trimesh(vertices=pts, faces=bf, process=False)
+            uniq, cnt = np.unique(m.edges_sorted, axis=0, return_counts=True)
+            bad = uniq[cnt != 2]
+            if len(bad) == 0:
+                return keep, m, m.is_watertight
+            added = 0
+            for a, b in bad:
+                fan = (st.tets == a).any(axis=1) & (st.tets == b).any(axis=1) & ~keep
+                keep |= fan
+                added += int(fan.sum())
+            if added == 0:                    # 无可补的四面体, 修复失败
+                return keep, m, False
+        return keep, m, m.is_watertight
+
+    keep, mesh, ok = try_extract(keep)
+    if not ok:
+        for _ in range(6):                    # 兜底: 逐步放大 alpha 重试
+            alpha = alpha * 1.1
+            k1, _, _ = st.status(alpha)
+            keep, mesh, ok = try_extract(k1)
+            print(f"[alpha] 局部修复不足, alpha 上调 10% -> {alpha:.4f}")
+            if ok:
+                break
+    if not mesh.is_watertight:
+        print("[warn] 仍未水密, 直接导出当前结果")
+
+    # ---- 外壳模式: 搜最小 alpha 使边界为单一 genus-0 闭合外壳 ----------
+    if args.mode == "outer" and mesh.euler_number != 2:
+        def pred(a):
+            """alpha=a 时边界(修复掐点后)是否已是单一 genus-0 外壳。"""
+            k, _, _ = st.status(a)
+            _, m, _ = try_extract(k)
+            parts = m.split(only_watertight=False)
+            outer = [p for p in parts if p.volume > 0]
+            if len(outer) != 1:
+                return False, None
+            m = outer[0]
+            if m.is_watertight and m.euler_number == 2:
+                return True, m
+            return False, None
+
+        a_lo, a_hi, found = alpha, None, None          # a_lo: 当前非外壳
+        a = alpha
+        for _ in range(20):                            # 指数上扫找可行 alpha
+            a = a * 1.3
+            okf, mf = pred(a)
+            if okf:
+                a_hi, found = a, mf
+                break
+        if a_hi is None:
+            print("[warn] 未找到 genus-0 外壳, 保持紧凑壳")
+        else:
+            for _ in range(8):                         # 二分细化到最小外壳 alpha
+                mid = (a_lo + a_hi) / 2
+                okf, mf = pred(mid)
+                if okf:
+                    a_hi, found = mid, mf
+                else:
+                    a_lo = mid
+            alpha, mesh = a_hi, found
+            print(f"[alpha] 外壳模式: 最小 genus-0 外壳 alpha = {alpha:.4f}")
+    vol0 = mesh.volume
+
+    # 去掉内腔壳 (负体积分量), 只留外壳
+    parts = mesh.split(only_watertight=False)
+    outer = [p for p in parts if p.volume > 0]
+    if len(outer) != len(parts):
+        print(f"[shell] 去除 {len(parts) - len(outer)} 个内腔面片")
+    mesh = max(outer, key=lambda m: m.volume) if len(outer) > 1 else outer[0]
+
+    # ---- Taubin 平滑 (不收缩) ------------------------------------------
+    if args.smooth > 0:
+        before = mesh.vertices.copy()
+        filter_taubin(mesh, lamb=0.5, nu=0.5, iterations=args.smooth)
+        drift = np.linalg.norm(mesh.vertices - before, axis=1)
+        print(f"[smooth] Taubin x{args.smooth}, 顶点漂移 mean={drift.mean():.4f} max={drift.max():.4f}")
+
+    # ---- 数值校验 ------------------------------------------------------
+    prox = trimesh.proximity.ProximityQuery(mesh)
+    d = np.abs(prox.signed_distance(pts))
+    print(f"[shell] 顶点 {len(mesh.vertices)}, 面 {len(mesh.faces)}, "
+          f"水密 {mesh.is_watertight}, 欧拉数 {mesh.euler_number}")
+    print(f"[shell] 体积 {mesh.volume:.1f} (平滑前 {vol0:.1f}), "
+          f"bbox {np.round(mesh.extents, 3)}")
+    print(f"[fit] 采样点到壳距离 |d|: mean={d.mean():.4f}, "
+          f"p99={np.percentile(d, 99):.4f}, max={d.max():.4f}")
+    return mesh
+
+
+# ------------------------------------------------------ 主流程
+def main():
+    ap = argparse.ArgumentParser(description="生成每个眼镜实例的最小壳 (alpha shape)")
+    here = os.path.dirname(os.path.abspath(__file__))
+    ap.add_argument("--obj", default=None,
+                    help="obj 路径; 缺省依次取 data.json 的 input 字段 / data 目录内唯一 obj")
+    ap.add_argument("--data", default=os.path.join(here, "data.json"),
+                    help="多实例 data.json (instances[].indices), 每实例单独处理")
+    ap.add_argument("--index", default=None,
+                    help="兼容旧的单实例 index.txt 顶点索引文件; 指定时优先于 --data")
+    ap.add_argument("--out", default=os.path.join(here, "shell.obj"),
+                    help="输出路径, 所有实例的壳合并导出为这一个文件 (默认 shell.obj)")
+    ap.add_argument("--mode", choices=["case", "outer", "tight"], default="case",
+                    help="case=眼镜盒式光滑凸外壳(最外侧连成一体), outer=跟随外形的 genus-0 外壳, "
+                         "tight=贴表面的紧身壳 (默认 case)")
+    ap.add_argument("--res", type=int, default=128, help="case 模式体素分辨率 (默认 128)")
+    ap.add_argument("--sigma", type=float, default=2.5, help="case 模式高斯平滑 sigma, 体素单位 (默认 2.5)")
+    ap.add_argument("--case-faces", type=int, default=25000, help="case 模式简化目标面数, 0=不简化 (默认 25000)")
+    ap.add_argument("--alpha", type=float, default=None,
+                    help="outer/tight 模式手动指定 alpha; 缺省自动搜索")
+    ap.add_argument("--smooth", type=int, default=10,
+                    help="Taubin 平滑迭代数, 0=关闭取严格最小壳 (默认 10)")
+    ap.add_argument("--filter", choices=["main", "all"], default="main",
+                    help="main=只保留最大空间簇(去 SAM3 噪声), all=用全部 index 点 (默认 main)")
+    ap.add_argument("--cluster-r", type=float, default=2.5,
+                    help="噪声过滤的聚类半径, 世界单位 (默认 2.5)")
+    ap.add_argument("--one-based", action="store_true",
+                    help="索引为 1-based (默认按 Blender 导出的 0-based 读取)")
+    args = ap.parse_args()
+
+    instances, input_obj = load_data(args)
+    obj_path = resolve_obj(args, input_obj)
+    verts, faces = load_obj(obj_path)
+    print(f"[load] {obj_path}: 顶点 {len(verts)}, 面 {len(faces)}, "
+          f"待处理实例 {len(instances)} 个")
+
+    meshes, n_ok = [], 0
+    for name, idx in instances:
+        print(f"\n===== 实例 {name}: {len(idx)} 顶点 =====")
+        try:
+            meshes.append(run_instance(verts, faces, idx, args))
+            n_ok += 1
+        except Exception as e:
+            print(f"[error] 实例 {name} 处理失败, 跳过: {e}")
+    if not meshes:
+        raise SystemExit("[error] 所有实例处理失败, 未导出任何壳")
+
+    # ---- 合并所有实例的壳 (同一坐标系, 直接拼接), 导出单个 obj ----
+    combined = meshes[0] if len(meshes) == 1 else trimesh.util.concatenate(meshes)
+    combined.export(args.out)
+    print(f"\n[summary] 完成 {n_ok}/{len(instances)} 个实例, 合并导出: {args.out} "
+          f"(顶点 {len(combined.vertices)}, 面 {len(combined.faces)}, "
+          f"连通分量 {len(combined.split())})")
+
+
+if __name__ == "__main__":
+    main()

fill2sc/sdf_fill.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+fill_gap_v3.py — 基于射线投射的眼镜-脸空隙填充
+"""
+
+import numpy as np
+from scipy.spatial import cKDTree
+from scipy.ndimage import binary_dilation, distance_transform_edt
+from skimage import measure
+import trimesh
+from trimesh.smoothing import filter_taubin
+import argparse
+import time
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--obj", required=True)
+    parser.add_argument("--index", required=True)
+    parser.add_argument("--out", default="fill_mesh.obj")
+    parser.add_argument("--res", type=int, default=60, help="分辨率（先跑低分辨率验证）")
+    parser.add_argument("--gap", type=float, default=1.5, help="填充间隙宽度 mm")
+    args = parser.parse_args()
+
+    t0 = time.time()
+    print("=== 眼镜-脸部空隙填充 v3 ===\n")
+
+    # 1. 加载
+    mesh = trimesh.load(args.obj, force="mesh")
+    V = mesh.vertices
+    F = mesh.faces
+    idx = np.loadtxt(args.index, dtype=np.int64) - 1
+    print(f"[load] 顶点 {len(V)}, 面 {len(F)}, 眼镜顶点 {len(idx)}")
+
+    # 2. 分类
+    is_glass = np.zeros(len(V), dtype=bool)
+    is_glass[idx] = True
+    glass_verts = V[idx]
+    face_verts = V[~is_glass]
+    print(f"[classify] 眼镜顶点 {len(glass_verts)}, 脸部顶点 {len(face_verts)}")
+
+    # 3. 对每个眼镜顶点，找最近的脸部顶点
+    face_tree = cKDTree(face_verts)
+    dists, nearest_idx = face_tree.query(glass_verts, k=1)
+    nearest_face_verts = face_verts[nearest_idx]
+    
+    # 方向向量：从脸部指向眼镜
+    directions = glass_verts - nearest_face_verts
+    dir_lengths = np.linalg.norm(directions, axis=1)
+    directions = directions / dir_lengths[:, None]
+    
+    # 统计平均方向
+    avg_dir = directions.mean(axis=0)
+    avg_dir = avg_dir / np.linalg.norm(avg_dir)
+    print(f"[dir] 平均方向（脸部→眼镜）: {avg_dir.round(3)}")
+
+    # 4. 确定填充范围
+    # 眼镜中心
+    glass_center = glass_verts.mean(axis=0)
+    # 沿方向投影
+    proj = (glass_verts - glass_center) @ avg_dir
+    front_pos = proj.max()  # 眼镜前表面（远离脸部）
+    back_pos = proj.min()   # 眼镜后表面（靠近脸部）
+    print(f"[surf] 前 {front_pos:.2f}, 后 {back_pos:.2f}")
+
+    # 5. 包围盒（只用眼镜区域）
+    margin = 4.0
+    lo = glass_verts.min(axis=0) - margin
+    hi = glass_verts.max(axis=0) + margin
+    print(f"[bbox] {lo.round(2)} -> {hi.round(2)}")
+
+    # 6. 体素网格（低分辨率测试）
+    shape = np.array([args.res] * 3)
+    pitch = (hi - lo).max() / args.res
+    xs = np.linspace(lo[0], hi[0], shape[0])
+    ys = np.linspace(lo[1], hi[1], shape[1])
+    zs = np.linspace(lo[2], hi[2], shape[2])
+    X, Y, Z = np.meshgrid(xs, ys, zs, indexing="ij")
+    points = np.stack([X.ravel(), Y.ravel(), Z.ravel()], axis=1)
+    print(f"[grid] {shape} = {points.shape[0]} 体素, 大小 {pitch:.3f}mm")
+
+    # 7. KD-Tree
+    glass_tree = cKDTree(glass_verts)
+    face_tree = cKDTree(face_verts)
+    print("[tree] KD-Tree 构建完成")
+
+    # 8. 计算距离
+    print("[dist] 计算距离场...")
+    dist_to_glass, _ = glass_tree.query(points, k=1)
+    dist_to_face, _ = face_tree.query(points, k=1)
+
+    # 9. 计算每个体素在"方向"上的投影
+    proj_val = (points - glass_center) @ avg_dir
+
+    # 10. 填充规则
+    # 在眼镜后表面附近（靠近脸部的那侧）
+    near_glass = dist_to_glass < args.gap * 2
+    
+    # 不在脸内部
+    outside_face = dist_to_face > 0.3
+    
+    # 在后表面附近（back_pos 到 back_pos + gap 之间）
+    in_gap = (proj_val > back_pos - 0.5) & (proj_val < back_pos + args.gap)
+    
+    # 组合
+    fill_grid = near_glass & outside_face & in_gap
+    fill_grid = fill_grid.reshape(shape)
+
+    n_fill = fill_grid.sum()
+    print(f"[fill] 填充体素: {n_fill}")
+
+    if n_fill < 30:
+        print("[warn] 太少，尝试仅用近眼镜条件...")
+        fill_grid = near_glass.reshape(shape)
+        n_fill = fill_grid.sum()
+        print(f"[fill] 仅近眼镜: {n_fill}")
+
+    if n_fill < 10:
+        print("[error] 填充为空")
+        return
+
+    # 11. 膨胀
+    fill_grid = binary_dilation(fill_grid, iterations=3)
+
+    # 12. MC
+    print("[mc] Marching Cubes...")
+    sdf = distance_transform_edt(~fill_grid).astype(np.float32)
+    sdf[fill_grid] = -distance_transform_edt(fill_grid)[fill_grid]
+
+    verts, faces, _, _ = measure.marching_cubes(
+        sdf, level=0.0,
+        spacing=(pitch, pitch, pitch)
+    )
+    verts += lo
+
+    result = trimesh.Trimesh(vertices=verts, faces=faces, process=True)
+
+    # 取最大分量
+    if len(result.split()) > 1:
+        comps = result.split(only_watertight=False)
+        result = max(comps, key=lambda m: m.volume)
+        print(f"[clean] 取最大分量")
+
+    filter_taubin(result, lamb=0.5, nu=0.5, iterations=5)
+
+    result.export(args.out)
+    print(f"\n[output] {args.out}")
+    print(f"  顶点: {len(result.vertices)}, 面: {len(result.faces)}")
+    print(f"  体积: {result.volume:.3f}, 水密: {result.is_watertight}")
+    print(f"  耗时: {time.time()-t0:.1f}s")
+
+
+if __name__ == "__main__":
+    main()

libs/IO/OBJ.cpp

@@ -48,6 +48,7 @@ ObjModel::MaterialLib::MaterialLib()
 
 bool ObjModel::MaterialLib::Save(const String& prefix, bool texLossless) const
 {
+	DEBUG_EXTRA("MaterialLib::Save %s", prefix.c_str());
 	std::ofstream out((prefix+".mtl").c_str());
 	if (!out.good())
 		return false;
@@ -130,7 +131,7 @@ bool ObjModel::MaterialLib::Load(const String& fileName)
 
 // S T R U C T S ///////////////////////////////////////////////////
 
-bool ObjModel::Save(const String& fileName, unsigned precision, bool texLossless) const
+bool ObjModel::Save(const String& fileName, unsigned precision, bool texLossless, bool bUseExistingUV) const
 {
 	if (vertices.empty())
 		return false;
@@ -140,6 +141,9 @@ bool ObjModel::Save(const String& fileName, unsigned precision, bool texLossless
 	if (!material_lib.Save(prefix, texLossless))
 		return false;
 
+	if (bUseExistingUV)
+		return true;
+
 	std::ofstream out((prefix + ".obj").c_str());
 	if (!out.good())
 		return false;
@@ -269,6 +273,129 @@ bool ObjModel::Load(const String& fileName)
 	return !vertices.empty();
 }
 
+bool ObjModel::LoadUV(const String& fileName, SEACAVE::cList<TexCoord,const TexCoord&,0,8192,uint32_t> &faceTexcoords, bool bLoadUV)
+{
+	DEBUG_EXTRA("LoadUV bLoadUV=%b", bLoadUV);
+    ASSERT(vertices.empty() && groups.empty() && material_lib.materials.empty());
+    std::ifstream fin(fileName.c_str());
+    String line, keyword;
+    std::istringstream in;
+    
+    while (fin.good()) {
+        std::getline(fin, line);
+        if (line.empty() || line[0u] == '#')
+            continue;
+        
+        in.str(line);
+        in >> keyword;
+        
+        if (keyword == "v") {
+            Vertex v;
+            in >> v[0] >> v[1] >> v[2];
+            if (in.fail())
+			{
+				DEBUG_EXTRA("1");
+                return false;
+			}
+            vertices.push_back(v);
+			// DEBUG_EXTRA("10, %d", vertices.size());
+        } else if (keyword == "vt") {
+            TexCoord vt;
+            in >> vt[0] >> vt[1];
+            if (in.fail())
+			{
+				DEBUG_EXTRA("2");
+                return false;
+			}
+            texcoords.push_back(vt);
+        } else if (keyword == "vn") {
+            Normal vn;
+            in >> vn[0] >> vn[1] >> vn[2];
+            if (in.fail())
+			{
+				DEBUG_EXTRA("3");
+                return false;
+			}
+            normals.push_back(vn);
+        } else if (keyword == "f") {
+            Face f;
+            memset(&f, 0xFF, sizeof(Face));
+            
+            for (size_t k = 0; k < 3; ++k) {
+                in >> keyword;
+                
+                // 更健壮的解析方法，处理各种索引格式
+                std::vector<String> parts;
+                size_t start = 0, end = 0;
+                
+                // 按'/'分割字符串
+                while ((end = keyword.find('/', start)) != String::npos) {
+                    parts.push_back(keyword.substr(start, end - start));
+                    start = end + 1;
+                }
+                parts.push_back(keyword.substr(start));
+                
+                // 根据分割后的部分数量处理不同情况
+                if (parts.size() >= 1 && !parts[0].empty()) {
+                    f.vertices[k] = std::stoi(parts[0]) - OBJ_INDEX_OFFSET;
+                }
+                
+                if (parts.size() >= 2 && !parts[1].empty()) {
+                    f.texcoords[k] = std::stoi(parts[1]) - OBJ_INDEX_OFFSET;
+                }
+                
+                if (parts.size() >= 3 && !parts[2].empty()) {
+                    f.normals[k] = std::stoi(parts[2]) - OBJ_INDEX_OFFSET;
+                }
+            }
+        
+            if (in.fail())
+			{
+				DEBUG_EXTRA("4");
+                return false;
+			}
+
+			if (bLoadUV)
+			{
+				for (int i = 0; i < 3; ++i) {
+					if (f.texcoords[i] != NO_ID && f.texcoords[i] < texcoords.size()) {
+						faceTexcoords.push_back(texcoords[f.texcoords[i]]);
+					} else {
+						// 索引无效（例如面定义中未提供vt索引或索引越界），使用默认值
+						faceTexcoords.push_back(Point2f(0.0f, 0.0f)); // 默认UV
+						DEBUG_EXTRA("Invalid texcoords %d", f.texcoords[i])
+					}
+				}
+
+				// DEBUG_EXTRA("faceTexcoords push_back [(%f,%f),(%f,%f),(%f,%f)]", texcoords[f.texcoords[0]].x, texcoords[f.texcoords[0]].y,
+				//	texcoords[f.texcoords[1]].x, texcoords[f.texcoords[1]].y, texcoords[f.texcoords[2]].x, texcoords[f.texcoords[2]].y);
+			}
+						
+            if (groups.empty())
+                AddGroup("");
+            groups.back().faces.push_back(f);
+        // } else if (keyword == "mtllib") {
+        //     in >> keyword;
+        //     if (!material_lib.Load(keyword))
+		// 		DEBUG_EXTRA("3");
+        //         return false;
+        } else if (keyword == "usemtl") {
+            Group group;
+            in >> group.material_name;
+            if (in.fail())
+			{
+				DEBUG_EXTRA("5");
+                return false;
+			}
+            groups.push_back(group);
+        }
+        
+        in.clear();
+    }
+    
+	DEBUG_EXTRA("6, vertices.size=%d, faceTexcoords.size=%d", vertices.size(), faceTexcoords.size());
+    return !vertices.empty();
+}
 
 ObjModel::Group& ObjModel::AddGroup(const String& material_name)
 {

libs/IO/OBJ.h

@@ -93,9 +93,10 @@ public:
 	ObjModel() {}
 
 	// Saves the obj model to an .obj file, its material lib and the materials with the given file name
-	bool Save(const String& fileName, unsigned precision=6, bool texLossless=false) const;
+	bool Save(const String& fileName, unsigned precision=6, bool texLossless=false, bool bUseExistingUV=false) const;
 	// Loads the obj model from an .obj file, its material lib and the materials with the given file name
 	bool Load(const String& fileName);
+	bool LoadUV(const String& fileName, SEACAVE::cList<TexCoord,const TexCoord&,0,8192,uint32_t> &faceTexcoords, bool bLoadUV = false);
 
 	// Creates a new group with the given material name
 	Group& AddGroup(const String& material_name);

libs/MVS/Mesh.cpp

@@ -1191,15 +1191,14 @@ namespace BasicPLY {
 } // namespace MeshInternal
 
 // import the mesh from the given file
-bool Mesh::Load(const String& fileName)
+bool Mesh::Load(const String& fileName, bool bLoadUV)
 {
 	TD_TIMER_STARTD();
 	const String ext(Util::getFileExt(fileName).ToLower());
 	bool ret;
 	if (ext == _T(".obj"))
-		ret = LoadOBJ(fileName);
+		ret = LoadOBJ(fileName, bLoadUV);
-	else
+	else if (ext == _T(".gltf") || ext == _T(".glb"))
-	if (ext == _T(".gltf") || ext == _T(".glb"))
 		ret = LoadGLTF(fileName, ext == _T(".glb"));
 	else
 		ret = LoadPLY(fileName);
@@ -1208,6 +1207,43 @@ bool Mesh::Load(const String& fileName)
 	DEBUG_EXTRA("Mesh loaded: %u vertices, %u faces (%s)", vertices.size(), faces.size(), TD_TIMER_GET_FMT().c_str());
 	return true;
 }
+
+void Mesh::CheckUVValid()
+{
+	for (int_t idxFace = 0; idxFace < (int_t)faces.size(); ++idxFace)
+	{
+		FOREACH(idxFace, faces)
+		{
+			const FIndex faceID = (FIndex)idxFace;
+			
+			const TexCoord* uv = &faceTexcoords[faceID * 3];
+
+			const Point2f& a = uv[0];
+			const Point2f& b = uv[1];
+			const Point2f& c = uv[2];
+
+			// DEBUG_EXTRA("a=(%f,%f),b=(%f,%f),c=(%f,%f)", a.x, a.y, b.x, b.y, c.x, c.y);
+
+			// 计算边向量
+			Point2f v0 = b - a;
+			Point2f v1 = c - a;
+			float denom = (v0.x * v0.x + v0.y * v0.y) * (v1.x * v1.x + v1.y * v1.y) - 
+						std::pow(v0.x * v1.x + v0.y * v1.y, 2);
+
+			// 处理退化三角形情况（面积接近0）
+			const float epsilon = 1e-10f;
+			if (std::abs(denom) < epsilon) 
+			{
+				// DEBUG_EXTRA("PointInTriangle - Degenerate triangle, denom=%.10f", denom);
+			}
+			else
+			{
+				// DEBUG_EXTRA("PointInTriangle Yes idxFace=%d", idxFace);
+			}
+		}
+	}
+}
+
 // import the mesh as a PLY file
 bool Mesh::LoadPLY(const String& fileName)
 {
@@ -1302,17 +1338,29 @@ bool Mesh::LoadPLY(const String& fileName)
 	return true;
 }
 // import the mesh as a OBJ file
-bool Mesh::LoadOBJ(const String& fileName)
+bool Mesh::LoadOBJ(const String& fileName, bool bLoadUV)
 {
 	ASSERT(!fileName.empty());
 	Release();
 
 	// open and parse OBJ file
 	ObjModel model;
-	if (!model.Load(fileName)) {
+	if (bLoadUV)
-		DEBUG_EXTRA("error: invalid OBJ file");
+	{
+		if (!model.LoadUV(fileName, faceTexcoords, true)) {
+			DEBUG_EXTRA("error: LoadUV invalid OBJ file %s", fileName.c_str());
 			return false;
 		}
+	}
+	else
+	{
+		if (!model.LoadUV(fileName, faceTexcoords)) {
+		// if (!model.Load(fileName)) {
+			DEBUG_EXTRA("error: Load invalid OBJ file %s", fileName.c_str());
+			return false;
+		}
+
+	}
 
 	if (model.get_vertices().empty() || model.get_groups().empty()) {
 		DEBUG_EXTRA("error: invalid mesh file");
@@ -1340,11 +1388,13 @@ bool Mesh::LoadOBJ(const String& fileName)
 		for (const ObjModel::Face& f: group.faces) {
 			ASSERT(f.vertices[0] != NO_ID);
 			faces.emplace_back(f.vertices[0], f.vertices[1], f.vertices[2]);
+			/*
 			if (f.texcoords[0] != NO_ID) {
 				for (int i=0; i<3; ++i)
 					faceTexcoords.emplace_back(model.get_texcoords()[f.texcoords[i]]);
 				faceTexindices.emplace_back((TexIndex)groupIdx);
 			}
+			*/
 			if (f.normals[0] != NO_ID) {
 				Normal& n = faceNormals.emplace_back(Normal::ZERO);
 				for (int i=0; i<3; ++i)
@@ -1445,13 +1495,13 @@ bool Mesh::LoadGLTF(const String& fileName, bool bBinary)
 /*----------------------------------------------------------------*/
 
 // export the mesh to the given file
-bool Mesh::Save(const String& fileName, const cList<String>& comments, bool bBinary) const
+bool Mesh::Save(const String& fileName, const cList<String>& comments, bool bBinary, bool bUseExistingUV) const
 {
 	TD_TIMER_STARTD();
 	const String ext(Util::getFileExt(fileName).ToLower());
 	bool ret;
 	if (ext == _T(".obj"))
-		ret = SaveOBJ(fileName);
+		ret = SaveOBJ(fileName, bUseExistingUV);
 	else
 	if (ext == _T(".gltf") || ext == _T(".glb"))
 		ret = SaveGLTF(fileName, ext == _T(".glb"));
@@ -1538,7 +1588,7 @@ bool Mesh::SavePLY(const String& fileName, const cList<String>& comments, bool b
 	return true;
 }
 // export the mesh as a OBJ file
-bool Mesh::SaveOBJ(const String& fileName) const
+bool Mesh::SaveOBJ(const String& fileName, bool bUseExistingUV) const
 {
 	ASSERT(!fileName.empty());
 	Util::ensureFolder(fileName);
@@ -1597,7 +1647,7 @@ bool Mesh::SaveOBJ(const String& fileName) const
 		pMaterial->diffuse_map = texturesDiffuse[idxTexture];
 	}
 
-	return model.Save(fileName, 6U, true);
+	return model.Save(fileName, 6U, true, bUseExistingUV);
 }
 // export the mesh as a GLTF file
 template <typename T>

libs/MVS/Mesh.h

@@ -262,8 +262,8 @@ public:
 	bool TransferTexture(Mesh& mesh, const FaceIdxArr& faceSubsetIndices={}, unsigned borderSize=3, unsigned textureSize=4096);
 
 	// file IO
-	bool Load(const String& fileName);
+	bool Load(const String& fileName, bool bLoadUV=false);
-	bool Save(const String& fileName, const cList<String>& comments=cList<String>(), bool bBinary=true) const;
+	bool Save(const String& fileName, const cList<String>& comments=cList<String>(), bool bBinary=true, bool bUseExistingUV=false) const;
 	bool Save(const FacesChunkArr&, const String& fileName, const cList<String>& comments=cList<String>(), bool bBinary=true) const;
 	static bool Save(const VertexArr& vertices, const String& fileName, bool bBinary=true);
 
@@ -271,13 +271,15 @@ public:
 	static inline VIndex GetVertex(const Face& f, VIndex v) { const uint32_t idx(FindVertex(f, v)); ASSERT(idx != NO_ID); return f[idx]; }
 	static inline VIndex& GetVertex(Face& f, VIndex v) { const uint32_t idx(FindVertex(f, v)); ASSERT(idx != NO_ID); return f[idx]; }
 
+	void CheckUVValid();
+
 protected:
 	bool LoadPLY(const String& fileName);
-	bool LoadOBJ(const String& fileName);
+	bool LoadOBJ(const String& fileName, bool bLoadUV);
 	bool LoadGLTF(const String& fileName, bool bBinary=true);
 
 	bool SavePLY(const String& fileName, const cList<String>& comments=cList<String>(), bool bBinary=true, bool bTexLossless=true) const;
-	bool SaveOBJ(const String& fileName) const;
+	bool SaveOBJ(const String& fileName, bool bUseExistingUV) const;
 	bool SaveGLTF(const String& fileName, bool bBinary=true) const;
 
 	#ifdef _USE_CUDA

libs/MVS/Scene.h

@@ -164,12 +164,27 @@ public:
 						 std::map<std::string, std::unordered_set<int>>& edge_faces_map,
 						 std::map<std::string, std::unordered_set<int>>& delete_edge_faces_map,
                          std::string& basePath);
+    bool LoadVisibleFacesData() {
+        std::map<std::string, std::unordered_set<int>> visible_faces_map;
+        std::unordered_set<int> face_visible_relative;
+        std::map<std::string, std::unordered_set<int>> edge_faces_map;
+        std::map<std::string, std::unordered_set<int>> delete_edge_faces_map;
+        std::string basePath = GetDefaultBasePath();  // 你需要实现这个函数
+        
+        return LoadVisibleFacesData(visible_faces_map, face_visible_relative, 
+                                   edge_faces_map, delete_edge_faces_map, basePath);
+    }
+    std::string GetDefaultBasePath() {
+        // 根据你的需求实现这个函数
+        // 例如：返回一个默认的路径，或者从配置中读取
+        return "default_path";
+    }
 		
 	// Mesh texturing
 	bool TextureMesh(unsigned nResolutionLevel, unsigned nMinResolution, unsigned minCommonCameras=0, float fOutlierThreshold=0.f, float fRatioDataSmoothness=0.3f,
 		bool bGlobalSeamLeveling=true, bool bLocalSeamLeveling=true, unsigned nTextureSizeMultiple=0, unsigned nRectPackingHeuristic=3, Pixel8U colEmpty=Pixel8U(255,127,39),
 		float fSharpnessWeight=0.5f, int ignoreMaskLabel=-1, int maxTextureSize=0, const IIndexArr& views=IIndexArr(), const SEACAVE::String& basename = "", bool bOriginFaceview = false,
-		const std::string& inputFileName = "", const std::string& meshFileName = "");
+		const std::string& inputFileName = "", const std::string& meshFileName = "", bool bUseExistingUV = false, const std::string& strUVMeshFileName = "");
 
 	std::string runPython(const std::string& command);
 	bool is_face_visible(const std::string& image_name, int face_index);

libs/MVS/calibrate.py

@@ -0,0 +1,120 @@
+"""
+calibrate.py —— 在跑 OpenMVS 之前，对输入照片做白平衡 + 曝光统一。
+这是消除"源视图色温差异"最彻底、最安全的办法：在像素进入纹理管线之前就对齐。
+用法: python calibrate.py <image_dir> <out_dir>
+
+做了两件事（都可选、都保守，不会破坏细节）：
+1) Gray-World 白平衡：把每张图的通道均值拉向全局中值（去色温差）
+2) 全局曝光归一化：把所有图的亮度中位数对齐到全局中位数（去曝光差）
+
+注意：OpenMVS 的 EstimateGlobalPhotometricCorrection 强依赖稠密点云，
+如果没有点云它会被跳过（你之前日志里 "Photometric: no point cloud, skip"）。
+所以在这里预处理是最稳的兜底。
+"""
+import os, sys, glob
+import numpy as np
+import cv2
+
+
+def gray_world(img_bgr, ref_bgr=None):
+    """灰度世界白平衡。
+
+    若给定 ref_bgr（全局参考图的 BGR 均值），则把本图通道均值拉向 ref；
+    否则用本图三通道的均值作为中性灰参考（单图自校正）。
+    不做任何“保守衰减”——必须完全对齐，否则等于没做。
+    """
+    b, g, r = [img_bgr[:, :, c].astype(np.float32) for c in range(3)]
+    mb, mg, mr = b.mean(), g.mean(), r.mean()
+    if ref_bgr is not None:
+        rb, rg, rr = ref_bgr
+    else:
+        # 单图自校正：以三通道共同均值作为中性灰目标
+        m = (mb + mg + mr) / 3.0
+        rb, rg, rr = m, m, m
+    kb = rb / mb if mb > 1e-6 else 1.0
+    kg = rg / mg if mg > 1e-6 else 1.0
+    kr = rr / mr if mr > 1e-6 else 1.0
+    # 限制增益上限，防止极端单图把噪声放大
+    def clamp_gain(k):
+        return min(max(k, 0.3), 3.0)
+    kb, kg, kr = clamp_gain(kb), clamp_gain(kg), clamp_gain(kr)
+    out = np.stack([
+        np.clip(b * kb, 0, 255),
+        np.clip(g * kg, 0, 255),
+        np.clip(r * kr, 0, 255),
+    ], axis=2).astype(np.uint8)
+    return out
+
+
+def normalize_exposure(img_bgr, target_median):
+    """曝光归一化：按亮度中位数线性缩放，对齐到全局目标中位数。
+    只做温和限制（上限 2.0），保证不同视图曝光真正一致。"""
+    gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY).astype(np.float32)
+    med = np.median(gray)
+    if med < 1.0:
+        return img_bgr
+    scale = target_median / med
+    scale = min(scale, 2.0)   # 防止极暗图放大噪声，但允许充分提亮
+    out = np.clip(img_bgr.astype(np.float32) * scale, 0, 255).astype(np.uint8)
+    return out
+
+
+def main():
+    if len(sys.argv) < 3:
+        print("usage: python calibrate.py <image_dir> <out_dir>")
+        sys.exit(1)
+    in_dir, out_dir = sys.argv[1], sys.argv[2]
+    os.makedirs(out_dir, exist_ok=True)
+
+    paths = sorted(glob.glob(os.path.join(in_dir, "*.*")))
+    paths = [p for p in paths if p.lower().endswith((".jpg", ".jpeg", ".png", ".tif", ".bmp"))]
+    if not paths:
+        print("no images found in", in_dir)
+        sys.exit(1)
+
+    # ---- 第一遍：统计所有图，确定全局参考（目标白平衡 + 目标亮度）----
+    means = []   # (mb, mg, mr)
+    meds = []
+    for p in paths:
+        img = cv2.imread(p)
+        if img is None:
+            continue
+        b, g, r = img[:, :, 0].astype(np.float32), img[:, :, 1].astype(np.float32), img[:, :, 2].astype(np.float32)
+        means.append((b.mean(), g.mean(), r.mean()))
+        gray = 0.299 * r + 0.587 * g + 0.114 * b
+        meds.append(np.median(gray))
+
+    means = np.array(means)
+    meds = np.array(meds)
+
+    # 全局参考 = 所有图通道均值的均值（= 数据集整体真实色彩），亮度取中位数
+    ref_b = means[:, 0].mean()
+    ref_g = means[:, 1].mean()
+    ref_r = means[:, 2].mean()
+    ref_bgr = (ref_b, ref_g, ref_r)
+    target_median = float(np.median(meds))
+
+    print(f"[calibrate] {len(paths)} images")
+    print(f"[calibrate] global reference white-balance (B,G,R) = ({ref_b:.1f}, {ref_g:.1f}, {ref_r:.1f})")
+    print(f"[calibrate] global reference luminance median = {target_median:.1f}")
+
+    # ---- 第二遍：所有图统一校正到同一参考 ----
+    for i, p in enumerate(paths):
+        img = cv2.imread(p)
+        if img is None:
+            continue
+
+        # 1) 白平衡：把本图拉向全局参考 ref_bgr
+        out = gray_world(img, ref_bgr=ref_bgr)
+
+        # 2) 曝光归一化：亮度中位数对齐到全局目标
+        out = normalize_exposure(out, target_median)
+
+        name = os.path.basename(p)
+        cv2.imwrite(os.path.join(out_dir, name), out)
+
+    print(f"[calibrate] done -> {out_dir}")
+
+
+if __name__ == "__main__":
+    main()