diff --git a/generate_fill_mesh_hsc.py b/generate_fill_mesh_hsc.py
new file mode 100644
index 0000000..7957495
--- /dev/null
+++ b/generate_fill_mesh_hsc.py
@@ -0,0 +1,1249 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+generate_fill_mesh.py
+=====================
+为 3D 打印模型生成"眼镜 <-> 脸部"之间镂空区域的填充网格。
+
+输入（与本脚本同目录）:
+    632723.obj   完整模型(封闭网格)
+    index.txt    眼镜区域的顶点索引(每行一个, 1-based)
+
+输出(同目录):
+    fill_mesh.obj        填充网格(独立、封闭、水密)
+    632723_filled.obj    原模型 + 填充 合并后的模型(可直接用于切片打印)
+    fill_preview.png     前视图深度预览(供人工检查)
+
+仅依赖 numpy 与 PIL。
+"""
+
+import numpy as np
+from collections import deque
+from PIL import Image, ImageDraw
+
+# ======================================================================
+# 参数配置
+# ======================================================================
+IN_OBJ      = "632723.obj"
+IN_IDX      = "index.txt"
+OUT_FILL    = "fill_mesh.obj"
+OUT_MERGED  = "632723_filled.obj"
+OUT_PREVIEW = "fill_preview.png"
+
+RES         = 0.1      # 栅格分辨率
+X0, X1      = -10.2, 10.2
+Y0, Y1      = 9.2, 19.8
+RY0, RY1    = 9.7, 19.3     # 填充区域 y 裁剪(只处理正面眼镜部分)
+SEP         = 0.25     # 区分前后表面的最小 z 间距
+FLUSH       = 0.15     # 填充前表面距镜框前面的距离(与镜框平齐, 留嵌入余量)
+EMBED_BOT   = 0.6      # 填充底面嵌入脸部深度
+SHELL_MAX   = 3.0      # 无真实脸面处的填充厚度上限(相对前表面)
+FACE_MIN    = 2.0      # 脸面低于此值视为误标伪面(后脑等), 不作填充底面
+CLAMP_BACK  = 0.4      # 距最后表面的安全距离(防穿出后脑)
+GAP_MIN     = 0.25     # 填充最小厚度
+SIGMA       = 2.0      # 高度场平滑 sigma(格)
+ERODE       = 2        # 足迹腐蚀(格) 0.2, 与膨胀0.3配合净外扩0.1(补偿边界平滑收缩)
+BSTEP       = 0.18     # 边界重采样步长
+BITER       = 25       # 边界 Laplacian 平滑迭代
+BLAM        = 0.6      # 边界平滑系数
+TOL         = 1e-4     # Jacobi 收敛阈值
+
+report = []
+def log(s):
+    print(s)
+    report.append(str(s))
+
+
+# ======================================================================
+# IO 工具
+# ======================================================================
+def parse_obj(path):
+    """解析 OBJ 文件, 返回顶点数组和面数组"""
+    vs, fs = [], []
+    with open(path) as f:
+        for line in f:
+            if line.startswith("v "):
+                p = line.split()
+                vs.append((float(p[1]), float(p[2]), float(p[3])))
+            elif line.startswith("f "):
+                p = line.split()
+                fs.append((int(p[1].split("/")[0]) - 1,
+                           int(p[2].split("/")[0]) - 1,
+                           int(p[3].split("/")[0]) - 1))
+    return np.asarray(vs), np.asarray(fs, dtype=np.int64)
+
+
+# ======================================================================
+# 形态学操作
+# ======================================================================
+def dilate_8(mask, iterations=1):
+    """二值图像膨胀 (8-邻域)"""
+    result = mask.copy()
+    for _ in range(iterations):
+        nm = result.copy()
+        nm[1:, :] |= result[:-1, :]; nm[:-1, :] |= result[1:, :]
+        nm[:, 1:] |= result[:, :-1]; nm[:, :-1] |= result[:, 1:]
+        nm[1:, 1:] |= result[:-1, :-1]; nm[:-1, :-1] |= result[1:, 1:]
+        nm[1:, :-1] |= result[:-1, 1:]; nm[:-1, 1:] |= result[1:, :-1]
+        result = nm
+    return result
+
+
+def erode_4(mask, iterations=1):
+    """二值图像腐蚀 (4-邻域)"""
+    result = mask.copy()
+    for _ in range(iterations):
+        ne = result.copy()
+        ne[1:, :] &= result[:-1, :]; ne[:-1, :] &= result[1:, :]
+        ne[:, 1:] &= result[:, :-1]; ne[:, :-1] &= result[:, 1:]
+        result = ne
+    return result
+
+
+def dilate_8_inf(field, iterations=1):
+    """浮点数场膨胀 (8-邻域取最大值)"""
+    result = field.copy()
+    for _ in range(iterations):
+        gf = result.copy()
+        for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)):
+            s = np.full_like(result, -np.inf)
+            if dj == 1: s[1:, :] = result[:-1, :]
+            elif dj == -1: s[:-1, :] = result[1:, :]
+            elif di == 1: s[:, 1:] = result[:, :-1]
+            elif di == -1: s[:, :-1] = result[:, 1:]
+            elif dj == 1 and di == 1: s[1:, 1:] = result[:-1, :-1]
+            elif dj == 1 and di == -1: s[1:, :-1] = result[:-1, 1:]
+            elif dj == -1 and di == 1: s[:-1, 1:] = result[1:, :-1]
+            else: s[:-1, :-1] = result[1:, 1:]
+            gf = np.maximum(gf, s)
+        result = gf
+    return result
+
+
+def erode_4_inf(field, iterations=1):
+    """浮点数场腐蚀 (4-邻域取最小值)"""
+    result = field.copy()
+    for _ in range(iterations):
+        mn = result.copy()
+        for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+            s = np.full_like(result, np.inf)
+            if dj == 1: s[1:, :] = result[:-1, :]
+            elif dj == -1: s[:-1, :] = result[1:, :]
+            elif di == 1: s[:, 1:] = result[:, :-1]
+            elif di == -1: s[:, :-1] = result[:, 1:]
+            mn = np.minimum(mn, s)
+        result = mn
+    return result
+
+
+def flood_outside(mask):
+    """从窗口边界洪泛, 标记外部; 未标记且非 mask 即为洞"""
+    outside = np.zeros_like(mask, bool)
+    dq = deque()
+    h, w = mask.shape
+    for i in range(w):
+        for j in (0, h - 1):
+            if not mask[j, i] and not outside[j, i]:
+                outside[j, i] = True; dq.append((j, i))
+    for j in range(h):
+        for i in (0, w - 1):
+            if not mask[j, i] and not outside[j, i]:
+                outside[j, i] = True; dq.append((j, i))
+    while dq:
+        j, i = dq.popleft()
+        for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+            nj, ni = j + dj, i + di
+            if 0 <= nj < h and 0 <= ni < w:
+                if not mask[nj, ni] and not outside[nj, ni]:
+                    outside[nj, ni] = True; dq.append((nj, ni))
+    return ~outside & ~mask
+
+
+def largest_component_8(mask):
+    """取 mask 的最大连通分量 (8-邻域)"""
+    labels = np.zeros(mask.shape, np.int32)
+    cur = 0
+    for j in range(mask.shape[0]):
+        for i in range(mask.shape[1]):
+            if mask[j, i] and labels[j, i] == 0:
+                cur += 1
+                q = deque([(j, i)])
+                labels[j, i] = cur
+                while q:
+                    y, x = q.popleft()
+                    for dy, dx in ((1,0),(-1,0),(0,1),(0,-1),
+                                   (1,1),(1,-1),(-1,1),(-1,-1)):
+                        ny, nx = y + dy, x + dx
+                        if (0 <= ny < mask.shape[0] and 0 <= nx < mask.shape[1]
+                                and mask[ny, nx] and labels[ny, nx] == 0):
+                            labels[ny, nx] = cur
+                            q.append((ny, nx))
+    sizes = np.bincount(labels.ravel())
+    sizes[0] = 0
+    main_label = int(np.argmax(sizes))
+    stray = int(mask.sum() - sizes[main_label])
+    return labels == main_label, stray
+
+
+# ======================================================================
+# 数值工具
+# ======================================================================
+def pbasis(x, y):
+    """二次多项式基函数: [1, x, y, x^2, xy, y^2]"""
+    return np.stack([np.ones_like(x), x, y, x * x, x * y, y * y], 1)
+
+
+def cross3(o, a, b):
+    """2D 叉积 (o->a) x (o->b)"""
+    return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
+
+
+def signed_volume(verts, faces):
+    """计算有符号体积"""
+    a = verts[faces[:, 0]]; b = verts[faces[:, 1]]; c = verts[faces[:, 2]]
+    return float(np.sum(np.einsum('ij,ij->i', a, np.cross(b, c))) / 6.0)
+
+
+def inside_mask(pts, msk):
+    """判断点是否在 mask 内"""
+    ii = np.clip(((pts[:, 0] - X0) / RES).astype(int), 0, msk.shape[1] - 1)
+    jj = np.clip(((pts[:, 1] - Y0) / RES).astype(int), 0, msk.shape[0] - 1)
+    return msk[jj, ii]
+
+
+def bilinear(grid, pts):
+    """双线性插值"""
+    fx = (pts[:, 0] - X0) / RES
+    fy = (pts[:, 1] - Y0) / RES
+    i = np.clip(np.floor(fx).astype(int), 0, grid.shape[1] - 2)
+    j = np.clip(np.floor(fy).astype(int), 0, grid.shape[0] - 2)
+    tx = np.clip(fx - i, 0, 1); ty = np.clip(fy - j, 0, 1)
+    return (grid[j, i] * (1 - tx) * (1 - ty) +
+            grid[j, i + 1] * tx * (1 - ty) +
+            grid[j + 1, i] * (1 - tx) * ty +
+            grid[j + 1, i + 1] * tx * ty)
+
+
+# ======================================================================
+# 平滑与插值
+# ======================================================================
+def smooth_valid(field, valid, sigma=SIGMA, it=1):
+    """归一化高斯卷积(只在 valid 上)"""
+    rad = int(3 * sigma)
+    k = np.exp(-0.5 * (np.arange(-rad, rad + 1) / sigma) ** 2)
+    field = field.copy()
+    for _ in range(it):
+        acc = np.zeros_like(field); accw = np.zeros_like(field)
+        val = valid & np.isfinite(field)
+        G = np.where(val, field, 0.0); M = val.astype(float)
+        tmp = np.zeros_like(G); tmpw = np.zeros_like(G)
+        for d in range(-rad, rad + 1):
+            if d == 0:
+                tmp += G * k[rad]; tmpw += M * k[rad]
+            elif d > 0:
+                tmp[d:, :] += G[:-d, :] * k[rad + d]; tmpw[d:, :] += M[:-d, :] * k[rad + d]
+                tmp[:-d, :] += G[d:, :] * k[rad + d]; tmpw[:-d, :] += M[d:, :] * k[rad + d]
+        acc += tmp; accw += tmpw
+        tmp = np.zeros_like(G); tmpw = np.zeros_like(G)
+        for d in range(-rad, rad + 1):
+            if d == 0:
+                tmp += acc * k[rad]; tmpw += accw * k[rad]
+            elif d > 0:
+                tmp[:, d:] += acc[:, :-d] * k[rad + d]; tmpw[:, d:] += accw[:, :-d] * k[rad + d]
+                tmp[:, :-d] += acc[:, d:] * k[rad + d]; tmpw[:, :-d] += accw[:, d:] * k[rad + d]
+        out = np.where(tmpw > 1e-9, tmp / np.maximum(tmpw, 1e-9), field)
+        field = np.where(val, out, field)
+    return field
+
+
+def laplace_fill(field, known, iters=6000):
+    """Dirichlet: known 固定, 其余解调和方程
+    缺失邻居(网格边缘)用反射(补 F 自身), 恒除以 4 保证系数和=1(收敛)"""
+    F = field.copy()
+    with np.errstate(all='ignore'):
+        mean_known = np.nanmean(F[known]) if known.any() else 0.0
+        if not np.isfinite(mean_known):
+            mean_known = 0.0
+        F[~known] = np.where(np.isfinite(F[~known]), F[~known], mean_known)
+        for it in range(iters):
+            avg = np.zeros_like(F)
+            for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+                s = np.roll(F, (dj, di), axis=(0, 1))
+                ok = np.ones_like(F, bool)
+                if dj == 1: ok[0, :] = False
+                if dj == -1: ok[-1, :] = False
+                if di == 1: ok[:, 0] = False
+                if di == -1: ok[:, -1] = False
+                avg += np.where(ok, s, F)
+            G = np.where(known, F, avg / 4.0)
+            df = np.abs(G - F)[~known]
+            delta = np.nanmax(df) if df.size and np.isfinite(df).any() else 0.0
+            F = G
+            if delta < TOL * RES:
+                break
+    return F
+
+
+# ======================================================================
+# 步骤 1: 加载模型并分类眼镜面
+# ======================================================================
+def load_and_classify():
+    """加载模型, 通过边连通主分量提取真眼镜物体"""
+    log("== 1. 读入模型 ==")
+    V, F = parse_obj(IN_OBJ)
+    idx = np.loadtxt(IN_IDX, dtype=np.int64) - 1
+    ism = np.zeros(len(V), bool)
+    ism[idx] = True
+    face_g = ism[F].any(1)
+    log(f"   顶点 {len(V)}, 面 {len(F)}, 眼镜标记顶点 {len(idx)}")
+
+    # 边连通主分量提取
+    emap = {}
+    for fi in np.where(face_g)[0]:
+        a, b, c = F[fi]
+        for u, v in ((a, b), (b, c), (c, a)):
+            e = (u, v) if u < v else (v, u)
+            emap.setdefault(e, []).append(fi)
+
+    seen = set()
+    best = []
+    for fi in np.where(face_g)[0]:
+        if fi in seen:
+            continue
+        comp, stack = [], [fi]
+        seen.add(fi)
+        while stack:
+            f0 = stack.pop()
+            comp.append(f0)
+            a, b, c = F[f0]
+            for u, v in ((a, b), (b, c), (c, a)):
+                e = (u, v) if u < v else (v, u)
+                for f2 in emap[e]:
+                    if f2 not in seen:
+                        seen.add(f2)
+                        stack.append(f2)
+        if len(comp) > len(best):
+            best = comp
+
+    face_g = np.zeros(len(F), bool)
+    face_g[best] = True
+    log(f"   眼镜物体 = 边连通主分量 {len(best)} 面, "
+        f"剥离误标皮肤 {int((ism[F].any(1)).sum() - len(best))} 面")
+    return V, F, face_g
+
+
+# ======================================================================
+# 步骤 2: 栅格化
+# ======================================================================
+def rasterize(V, F, face_g):
+    """栅格化三角形到高度场"""
+    W = int(round((X1 - X0) / RES)) + 1
+    H = int(round((Y1 - Y0) / RES)) + 1
+    log(f"== 2. 栅格化 (窗口 x[{X0},{X1}] y[{Y0},{Y1}], res={RES}, {W}x{H}) ==")
+
+    gz1 = np.full((H, W), -np.inf)   # 眼镜最前表面
+    gz2 = np.full((H, W), -np.inf)   # 眼镜第二表面(镜框管背面)
+    fz  = np.full((H, W), -np.inf)   # 非眼镜最前表面(脸)
+    bz  = np.full((H, W),  np.inf)   # 全网格最后表面(后脑)
+
+    inwin = ((V[:, 0] > X0 - 0.5) & (V[:, 0] < X1 + 0.5) &
+             (V[:, 1] > Y0 - 0.5) & (V[:, 1] < Y1 + 0.5))
+    fwin = inwin[F].all(1)
+    log(f"   窗口内三角形 {int(fwin.sum())} (其中眼镜 {(face_g & fwin).sum()})")
+
+    A, B, C = V[F[:, 0]], V[F[:, 1]], V[F[:, 2]]
+    for t in np.where(fwin)[0]:
+        ax, ay, az = A[t]; bx, by, bz_ = B[t]; cx, cy, cz = C[t]
+        g = face_g[t]
+        i0 = max(int((min(ax, bx, cx) - X0) / RES), 0)
+        i1 = min(int((max(ax, bx, cx) - X0) / RES), W - 1)
+        j0 = max(int((min(ay, by, cy) - Y0) / RES), 0)
+        j1 = min(int((max(ay, by, cy) - Y0) / RES), H - 1)
+        if i1 < i0 or j1 < j0:
+            continue
+        v0x, v0y = cx - ax, cy - ay
+        v1x, v1y = bx - ax, by - ay
+        d00 = v0x * v0x + v0y * v0y
+        d01 = v0x * v1x + v0y * v1y
+        d11 = v1x * v1x + v1y * v1y
+        den = d00 * d11 - d01 * d01
+        if abs(den) < 1e-14:
+            continue
+        for j in range(j0, j1 + 1):
+            py = Y0 + j * RES
+            for i in range(i0, i1 + 1):
+                px = X0 + i * RES
+                v2x, v2y = px - ax, py - ay
+                d02 = v0x * v2x + v0y * v2y
+                d12 = v1x * v2x + v1y * v2y
+                u = (d11 * d02 - d01 * d12) / den
+                v = (d00 * d12 - d01 * d02) / den
+                if u < 0 or v < 0 or u + v > 1:
+                    continue
+                z = az + u * (cz - az) + v * (bz_ - az)
+                if g:
+                    if z > gz1[j, i] + 1e-9:
+                        if z - gz1[j, i] > SEP and gz1[j, i] > -np.inf:
+                            gz2[j, i] = max(gz2[j, i], gz1[j, i])
+                        gz1[j, i] = z
+                    elif z < gz1[j, i] - SEP and z > gz2[j, i]:
+                        gz2[j, i] = z
+                else:
+                    if z > fz[j, i]:
+                        fz[j, i] = z
+                if z < bz[j, i]:
+                    bz[j, i] = z
+
+    have_g1 = np.isfinite(gz1) & (gz1 > -1e30)
+    have_g2 = np.isfinite(gz2) & (gz2 > -1e30)
+    have_f  = np.isfinite(fz) & (fz > -1e30)
+    have_b  = np.isfinite(bz) & (bz < 1e30)
+    g1f = have_g1
+    log(f"   有眼镜表面格 {have_g1.sum()}, 管状(双面) {have_g2.sum()}, "
+        f"有脸面 {have_f.sum()}, 有背面 {have_b.sum()}")
+
+    return gz1, gz2, fz, bz, have_g1, have_g2, have_f, have_b, g1f, W, H
+
+
+# ======================================================================
+# 步骤 3: 构建足迹 mask 与填充区域 R
+# ======================================================================
+def build_footprint(g1f):
+    """构建眼镜足迹 mask 和填充区域 R"""
+    log("== 3. 眼镜足迹 / 填充区域 R ==")
+    mask = g1f.copy()
+
+    # 取主分量
+    mask, n_stray = largest_component_8(mask)
+    log(f"   足迹取主分量: 剔除杂散块 {n_stray} 格 (误标到镜框外的皮肤/后脑)")
+
+    # 中央区剪除(鼻侧多余填充)
+    holes = ~mask & ~flood_outside(mask)
+    hl = np.zeros(mask.shape, np.int32)
+    big_holes = []
+    for j in range(mask.shape[0]):
+        for i in range(mask.shape[1]):
+            if holes[j, i] and hl[j, i] == 0:
+                hl[j, i] = 1
+                q = deque([(j, i)])
+                pts = [(j, i)]
+                while q:
+                    y, x = q.popleft()
+                    for dy, dx in ((1,0),(-1,0),(0,1),(0,-1),
+                                   (1,1),(1,-1),(-1,1),(-1,-1)):
+                        ny, nx = y + dy, x + dx
+                        if (0 <= ny < mask.shape[0] and 0 <= nx < mask.shape[1]
+                                and holes[ny, nx] and hl[ny, nx] == 0):
+                            hl[ny, nx] = 1
+                            q.append((ny, nx))
+                            pts.append((ny, nx))
+                if len(pts) >= 500:
+                    big_holes.append(pts)
+
+    if len(big_holes) >= 2:
+        big_holes.sort(key=len, reverse=True)
+        h_l, h_r = sorted(big_holes[:2],
+                          key=lambda pts: sum(p[1] for p in pts) / len(pts))
+        bl = (min(p[1] for p in h_l), max(p[1] for p in h_l), min(p[0] for p in h_l))
+        br = (min(p[1] for p in h_r), max(p[1] for p in h_r), min(p[0] for p in h_r))
+        x_in_l, x_in_r = bl[1], br[0]
+        y_cut = min(bl[2], br[2])
+        if x_in_l < x_in_r:
+            cut = np.zeros_like(mask, bool)
+            cut[:y_cut, x_in_l + 1:x_in_r] = True
+            n_cut = int((mask & cut).sum())
+            mask &= ~cut
+            log(f"   中央区剪除(两镜片开口之间, 开口底缘 y>{Y0 + y_cut * RES:.1f} 以下): "
+                f"剔除 {n_cut} 格误标鼻面")
+    else:
+        log("   ! 未找到两个镜片开口, 跳过中央区剪除")
+
+    # 膨胀 3 格
+    mask = dilate_8(mask, 3)
+
+    # 补内部洞 (镜片开口)
+    holes = flood_outside(mask)
+    log(f"   足迹 {mask.sum()} 格, 补内部洞 {holes.sum()} 格 (镜片开口)")
+    mask = mask | holes
+
+    # 腐蚀
+    eroded = erode_4(mask, ERODE)
+
+    # y 裁剪 + 最大连通块
+    H, W = eroded.shape
+    jj = np.arange(H)
+    eroded = eroded & (jj[:, None] >= int((RY0 - Y0) / RES)) & \
+                      (jj[:, None] <= int((RY1 - Y0) / RES))
+
+    R = np.zeros_like(eroded)
+    seen = np.zeros_like(eroded, bool)
+    best = None
+    for j0 in range(H):
+        for i0 in range(W):
+            if eroded[j0, i0] and not seen[j0, i0]:
+                comp = []
+                dq = deque([(j0, i0)])
+                seen[j0, i0] = True
+                touch = False
+                while dq:
+                    j, i = dq.popleft()
+                    comp.append((j, i))
+                    if i in (0, W - 1) or j in (0, H - 1):
+                        touch = True
+                    for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+                        nj, ni = j + dj, i + di
+                        if 0 <= nj < H and 0 <= ni < W and eroded[nj, ni] and not seen[nj, ni]:
+                            seen[nj, ni] = True
+                            dq.append((nj, ni))
+                if best is None or len(comp) > len(best[0]):
+                    best = (comp, touch)
+
+    if best is None:
+        raise SystemExit("R 为空: 足迹检测失败")
+    comp, touch = best
+    for j, i in comp:
+        R[j, i] = True
+    log(f"   R: {R.sum()} 格 ({R.sum()*RES*RES:.2f} 面积单位), "
+        f"触碰窗口边界={touch} (触碰=侧墙切在头内部, 正常)")
+    if R.sum() < 200:
+        raise SystemExit("R 过小")
+
+    return mask, R
+
+
+# ======================================================================
+# 步骤 4: 构造填充前/后高度场
+# ======================================================================
+def build_height_fields(gz1, gz2, fz, bz, have_g1, have_f, have_b, g1f, mask, R):
+    """构造填充前/后高度场"""
+    log("== 4. 构造填充前/后高度场 ==")
+    H, W = gz1.shape
+    gx, gy = np.meshgrid(X0 + np.arange(W) * RES, Y0 + np.arange(H) * RES)
+
+    # ---- 修补眼镜表面 ----
+    g1c = dilate_8(g1f, 2)
+    g1c = erode_4(g1c, 2)
+    g1c |= g1f
+
+    # 修补针孔
+    gz1f = np.where(g1f, gz1, -np.inf)
+    nb = []
+    for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)):
+        s = np.full_like(gz1, np.nan)
+        if dj == 1: s[1:, :] = gz1[:-1, :]
+        elif dj == -1: s[:-1, :] = gz1[1:, :]
+        elif di == 1: s[:, 1:] = gz1[:, :-1]
+        elif di == -1: s[:, :-1] = gz1[:, 1:]
+        elif dj == 1 and di == 1: s[1:, 1:] = gz1[:-1, :-1]
+        elif dj == 1 and di == -1: s[1:, :-1] = gz1[:-1, 1:]
+        elif dj == -1 and di == 1: s[:-1, 1:] = gz1[1:, :-1]
+        else: s[:-1, :-1] = gz1[1:, 1:]
+        nb.append(s)
+    nb = np.stack(nb)
+    valid_nb = nb > -1e29
+    with np.errstate(all='ignore'):
+        nbmed = np.nanmedian(np.where(valid_nb, nb, np.nan), axis=0)
+    pin = g1f & np.isfinite(nbmed) & (gz1 < nbmed - 0.4)
+
+    gz1d = dilate_8_inf(gz1f, 3)
+    gz1f = np.where(g1f & ~pin, gz1, gz1d)
+    log(f"   顶面针孔修补: {int(pin.sum())} 格 (孤立低谷, 用邻域最大值补回顶面)")
+    nfill = int((g1c & ~g1f & R & (gz1f >= 7.0)).sum())
+    log(f"   镜框带补栅格化角落缺口: {nfill} 格")
+
+    # ---- 框前面拟合 ----
+    mfit = g1c & R & (gz1f >= 7.0)
+
+    # 框带定义
+    eR = erode_4(R, 3)
+    outer_band = R & ~eR
+    frame_band = (g1c & R) | (outer_band & (gz1f > -1e29))
+
+    # 陡斜坡外缘钳制
+    mdil = np.where(g1f, gz1, np.inf)
+    mdil = erode_4_inf(mdil, 3)
+    fringe = frame_band & ~g1f
+    gz1f = np.where(fringe & (mdil < 1e29), np.minimum(gz1f, mdil), gz1f)
+    gz1f = np.where(fringe, smooth_valid(gz1f, fringe, sigma=2.0, it=1), gz1f)
+
+    # 稳健二次拟合
+    xp, yp, zp = gx[mfit], gy[mfit], gz1f[mfit]
+    wt = np.ones(len(zp))
+    for _ in range(3):
+        Aw = pbasis(xp, yp) * wt[:, None]
+        coef, *_ = np.linalg.lstsq(Aw, zp * wt, rcond=None)
+        rfit = zp - pbasis(xp, yp) @ coef
+        wt = 1.0 / (1.0 + (np.abs(rfit) / 0.5) ** 2)
+    zfit = (pbasis(gx.ravel(), gy.ravel()) @ coef).reshape(H, W)
+    rf = np.abs((zfit - gz1f)[mfit])
+    log(f"   框前面拟合(稳健): 残差 mean {rf.mean():.3f}, p90 {np.percentile(rf, 90):.3f}, "
+        f"max {rf.max():.3f}")
+
+    # 残差场调和延拓
+    mfit2 = mfit & (np.abs(gz1f - zfit) <= 1.0)
+    resf = np.full((H, W), np.nan)
+    resf[mfit2] = gz1f[mfit2] - zfit[mfit2]
+    resf = smooth_valid(resf, mfit2, sigma=2.5)
+    Rext = laplace_fill(resf, mfit2)
+    Rext = np.where(R, Rext, np.nan)
+
+    # ---- 前表面 ----
+    hf = np.full((H, W), np.nan)
+    hf[frame_band] = gz1f[frame_band] - FLUSH
+    openf = R & ~frame_band
+    hfo = np.where(openf, zfit + Rext - FLUSH, np.nan)
+
+    # 开口区安全钳制
+    gmax2 = np.where(mfit, gz1f, -np.inf)
+    gmax2 = dilate_8_inf(gmax2, 3)
+    hfo = np.where(openf & (gmax2 > -1e29), np.minimum(hfo, gmax2 - FLUSH), hfo)
+    hfo = smooth_valid(hfo, openf, sigma=2.0, it=2)
+    hf = np.where(openf & np.isfinite(hfo), hfo, hf)
+    hf = smooth_valid(hf, R, sigma=1.5)
+
+    # 墙区安全钳制
+    edge_in = erode_4(R, 4)
+    wall_zone = R & ~edge_in
+    hf = np.where(frame_band & wall_zone, np.minimum(hf, gz1f - FLUSH), hf)
+    hf = np.where(frame_band & ~wall_zone, np.minimum(hf, gz1f + 0.10), hf)
+
+    # 交界连续性检查
+    jn, jn_at = 0.0, None
+    bflat = mfit2 & (gz1f > zfit - 0.3)
+    for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+        a = np.roll(bflat, (dj, di), axis=(0, 1)) & openf & np.isfinite(hf)
+        if a.any():
+            b = np.roll(a, (-dj, -di), axis=(0, 1))
+            dd = np.abs(np.where(a, hf, 0.0)[a] - np.where(np.isfinite(hf), hf, 0.0)[b])
+            k = int(np.nanargmax(dd))
+            if dd[k] > jn:
+                ja = np.argwhere(a)[k]
+                jn, jn_at = float(dd[k]), (X0 + ja[1] * RES, Y0 + ja[0] * RES)
+    log(f"   交界处高度跳变最大 {jn:.3f} @ {jn_at} (平面前框带接缝应<0.3; "
+        "镜圈圆角处~0.4为镜框自身几何, 并集后不暴露)")
+
+    # ---- 后表面 ----
+    hb = np.full((H, W), np.nan)
+    real_f = have_f & (fz >= FACE_MIN)
+    hb[real_f] = fz[real_f] - EMBED_BOT
+    knownb = R & real_f
+    hb = laplace_fill(hb, knownb)
+    hb = smooth_valid(hb, R)
+
+    cap = hf - SHELL_MAX
+    ncap = int((R & ~real_f & np.isfinite(hb) & (hb < cap)).sum())
+    hb = np.where(R & ~real_f, np.maximum(hb, cap), hb)
+    log(f"   厚度上限(无真实脸面处, 距前面>{SHELL_MAX})生效格数: {ncap}")
+
+    hb = np.where(R & real_f, np.minimum(hb, fz - 0.25), hb)
+    bzs = smooth_valid(np.where(have_b, bz, np.nan), have_b)
+    hb = np.where(R, hb, np.nan)
+    hb = np.where(have_b, np.maximum(hb, bzs + CLAMP_BACK), hb)
+    hb = np.minimum(hb, hf - GAP_MIN)
+
+    # 外扩填充 NaN
+    for _ in range(60):
+        nanm = ~np.isfinite(hf)
+        if not nanm.any(): break
+        for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+            s = np.roll(np.where(nanm, 0, hf), (dj, di), axis=(0, 1))
+            ok = ~np.roll(nanm, (dj, di), axis=(0, 1)) & np.isfinite(s)
+            hf = np.where(nanm & ok, s, hf)
+    for _ in range(60):
+        nanm = ~np.isfinite(hb)
+        if not nanm.any(): break
+        for dj, di in ((1,0),(-1,0),(0,1),(0,-1)):
+            s = np.roll(np.where(nanm, 0, hb), (dj, di), axis=(0, 1))
+            ok = ~np.roll(nanm, (dj, di), axis=(0, 1)) & np.isfinite(s)
+            hb = np.where(nanm & ok, s, hb)
+
+    thickm = np.where(R, hf - hb, np.nan)
+    log(f"   填充厚度: min {np.nanmin(thickm):.2f}, 中位 {np.nanmedian(thickm):.2f}, "
+        f"mean {np.nanmean(thickm):.2f}, max {np.nanmax(thickm):.2f}")
+
+    return hf, hb, frame_band, wall_zone, gz1f, bzs, real_f, fz
+
+
+# ======================================================================
+# 步骤 5: 边界提取与平滑
+# ======================================================================
+def extract_and_smooth_boundary(R, mask, g1f):
+    """提取并平滑边界"""
+    log("== 5. 边界提取与平滑 ==")
+    H, W = R.shape
+
+    # 裂缝边链成回路
+    edges = {}
+    def add_edge(a, b):
+        edges.setdefault(a, []).append(b)
+    for j in range(H):
+        for i in range(W):
+            if not R[j, i]:
+                continue
+            if i == 0 or not R[j, i - 1]:
+                add_edge((i, j), (i, j + 1))
+            if i == W - 1 or not R[j, i + 1]:
+                add_edge((i + 1, j + 1), (i + 1, j))
+            if j == 0 or not R[j - 1, i]:
+                add_edge((i + 1, j), (i, j))
+            if j == H - 1 or not R[j + 1, i]:
+                add_edge((i, j + 1), (i + 1, j + 1))
+
+    if not edges:
+        raise SystemExit("无边界")
+    start = next(iter(edges))
+    loop = [start]
+    cur = edges[start].pop(0)
+    used = {(start, cur)}
+    while cur != start:
+        loop.append(cur)
+        nxts = [e for e in edges.get(cur, []) if (cur, e) not in used]
+        if not nxts:
+            break
+        nxt = nxts[0]
+        used.add((cur, nxt))
+        cur = nxt
+    P = np.array([[X0 + i * RES, Y0 + j * RES] for (i, j) in loop], float)
+    area2 = np.sum(P[:, 0] * np.roll(P[:, 1], -1) - np.roll(P[:, 0], -1) * P[:, 1])
+    if area2 < 0:
+        P = P[::-1]
+    log(f"   边界回路 {len(P)} 点, 周长 {np.linalg.norm(np.roll(P,-1,0)-P,axis=1).sum():.2f}")
+
+    def resample(P, step):
+        d = np.linalg.norm(np.roll(P, -1, 0) - P, axis=1)
+        L = np.concatenate([[0], np.cumsum(d)])
+        total = L[-1]
+        n = max(8, int(round(total / step)))
+        t = np.linspace(0, total, n, endpoint=False)
+        x = np.interp(t, L, np.concatenate([P[:, 0], P[:1, 0]]))
+        y = np.interp(t, L, np.concatenate([P[:, 1], P[:1, 1]]))
+        return np.stack([x, y], 1)
+
+    def roughness(P):
+        d = np.roll(P, -1, 0) - P
+        a = np.arctan2(d[:, 1], d[:, 0])
+        da = np.abs((a - np.roll(a, -1) + np.pi) % (2 * np.pi) - np.pi)
+        return float(da.mean()), float(da.std())
+
+    r0 = roughness(P)
+    B = resample(P, BSTEP)
+    for _ in range(BITER):
+        B = (1 - BLAM) * B + BLAM * 0.5 * (np.roll(B, 1, 0) + np.roll(B, -1, 0))
+
+    # 面积恢复
+    areaR = R.sum() * RES * RES
+    for _ in range(30):
+        aB = 0.5 * abs(np.sum(B[:, 0] * np.roll(B[:, 1], -1) - np.roll(B[:, 0], -1) * B[:, 1]))
+        if aB >= 0.97 * areaR:
+            break
+        c = B.mean(0)
+        s = np.sqrt(0.97 * areaR / max(aB, 1e-9))
+        s = min(s, 1.05)
+        B = c + (B - c) * s
+
+        B = resample(B, BSTEP)
+    r1 = roughness(B)
+    log(f"   边界粗糙度(平均转折角/标准差): 平滑前 {np.degrees(r0[0]):.2f}°/{np.degrees(r0[1]):.2f}° "
+        f"-> 平滑后 {np.degrees(r1[0]):.2f}°/{np.degrees(r1[1]):.2f}°")
+
+    # 边界须压在镜框带上
+    bad = ~inside_mask(B, mask)
+    if bad.any():
+        c = B.mean(0)
+        for k in np.where(bad)[0]:
+            p = B[k]
+            for s in np.linspace(0.1, 0.9, 30):
+                q = c + s * (p - c)
+                if inside_mask(q[None, :], mask)[0]:
+                    B[k] = q
+                    break
+        for _ in range(10):
+            B = (1 - BLAM) * B + BLAM * 0.5 * (np.roll(B, 1, 0) + np.roll(B, -1, 0))
+        B = resample(B, BSTEP)
+
+    bii = np.clip(((B[:, 0] - X0) / RES).astype(int), 0, W - 1)
+    bjj = np.clip(((B[:, 1] - Y0) / RES).astype(int), 0, H - 1)
+    nb = np.zeros(len(B), bool)
+    for dj, di in ((0,0),(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)):
+        jj2 = np.clip(bjj + dj, 0, H - 1); ii2 = np.clip(bii + di, 0, W - 1)
+        nb |= g1f[jj2, ii2]
+    log(f"   平滑边界 {len(B)} 点, 超出足迹 {int((~inside_mask(B, mask)).sum())}, "
+        f"压在镜框带上 {nb.mean()*100:.1f}%")
+
+    return B, loop
+
+
+# ======================================================================
+# 步骤 6: 三角剖分 (耳切法 + 内部点插入)
+# ======================================================================
+def ear_clip(P):
+    """P 为 CCW 简单多边形, 返回三角形索引列表"""
+    idx = list(range(len(P)))
+    tris = []
+    guard = 0
+    while len(idx) > 3 and guard < 5000:
+        guard += 1
+        m = len(idx)
+        best_k, best_score = None, None
+        for k in range(m):
+            i0, i1, i2 = idx[(k - 1) % m], idx[k], idx[(k + 1) % m]
+            a, b, c = P[i0], P[i1], P[i2]
+            cr = cross3(a, b, c)
+            if cr <= 1e-10:
+                continue
+            inside_any = False
+            for j in idx:
+                if j in (i0, i1, i2):
+                    continue
+                p = P[j]
+                if (cross3(a, b, p) >= -1e-12 and cross3(b, c, p) >= -1e-12
+                        and cross3(c, a, p) >= -1e-12):
+                    inside_any = True
+                    break
+            if not inside_any:
+                lab = np.hypot(*(np.subtract(b, a))); lac = np.hypot(*(np.subtract(c, a)))
+                lbc = np.hypot(*(np.subtract(c, b)))
+                score = cr / max(lab * lac, lab * lbc, lac * lbc, 1e-12)
+                if best_score is None or score > best_score:
+                    best_k, best_score = k, score
+        if best_k is None:
+            m = len(idx)
+            areas = [(cross3(P[idx[(k-1)%m]], P[idx[k]], P[idx[(k+1)%m]]), k)
+                     for k in range(m)]
+            areas.sort(reverse=True)
+            best_k = areas[0][1]
+            log("   ! 耳切兜底触发")
+        i0, i1, i2 = (idx[(best_k - 1) % len(idx)], idx[best_k],
+                      idx[(best_k + 1) % len(idx)])
+        tris.append((i0, i1, i2))
+        idx.pop(best_k)
+    tris.append((idx[0], idx[1], idx[2]))
+    return tris
+
+
+def triangulate_fill(B, R, hf, hb):
+    """对填充区域进行三角剖分"""
+    log("== 6. 区域三角化 (耳切法 + 内部网格点插入, 无翻转风险) ==")
+    H, W = R.shape
+    Nb = len(B)
+
+    # 耳切三角化
+    T = np.asarray(ear_clip(B.tolist()), np.int64)
+    log(f"   耳切三角化: {len(T)} 三角形")
+
+    # 内部点候选
+    inner2 = R.copy()
+    for _ in range(2):
+        ne = inner2.copy()
+        ne[1:, :] &= inner2[:-1, :]; ne[:-1, :] &= inner2[1:, :]
+        ne[:, 1:] &= inner2[:, :-1]; ne[:, :-1] &= inner2[:, 1:]
+        inner2 = ne
+    step = max(1, int(round(0.3 / RES)))
+    cand = []
+    for j in range(0, H, step):
+        for i in range(0, W, step):
+            if inner2[j, i]:
+                cand.append((X0 + i * RES, Y0 + j * RES))
+    cand = np.asarray(cand)
+    log(f"   内部插入点候选 {len(cand)}")
+
+    # 逐点插入
+    xy = np.vstack([B, cand])
+    n_cand = len(cand)
+    extra = 0
+    Ta = [list(map(int, t)) for t in T]
+    for ci in range(n_cand):
+        p = xy[Nb + ci]
+        m = len(Ta)
+        TA = np.asarray(Ta)
+        a = xy[TA[:, 0]]; b = xy[TA[:, 1]]; c = xy[TA[:, 2]]
+        d = ((b[:, 1] - c[:, 1]) * (a[:, 0] - c[:, 0]) + (c[:, 0] - b[:, 0]) * (a[:, 1] - c[:, 1]))
+        with np.errstate(divide='ignore', invalid='ignore'):
+            l1 = ((b[:, 1] - c[:, 1]) * (p[0] - c[:, 0]) + (c[:, 0] - b[:, 0]) * (p[1] - c[:, 1])) / d
+            l2 = ((c[:, 1] - a[:, 1]) * (p[0] - c[:, 0]) + (a[:, 0] - c[:, 0]) * (p[1] - c[:, 1])) / d
+        l3 = 1 - l1 - l2
+        hit = np.where((l1 > 5e-3) & (l2 > 5e-3) & (l3 > 5e-3)
+                       & (l1 < 1 - 5e-3) & (l2 < 1 - 5e-3) & (l3 < 1 - 5e-3))[0]
+        if len(hit) == 0:
+            continue
+        t = int(hit[0])
+        i0, i1, i2 = Ta[t]
+        vnew = Nb + ci
+        Ta[t] = [i0, i1, vnew]
+        Ta.append([i1, i2, vnew])
+        Ta.append([i2, i0, vnew])
+        extra += 1
+    T = np.asarray([t for t in Ta if t[0] != t[1] and t[1] != t[2] and t[0] != t[2]], np.int64)
+    xy = xy[:Nb + n_cand]
+
+    # 内部顶点轻度 Laplacian 平滑
+    es = np.unique(np.sort(np.concatenate([T[:, [0, 1]], T[:, [1, 2]], T[:, [2, 0]]]), axis=1), axis=0)
+    src = np.concatenate([es[:, 0], es[:, 1]])
+    dst = np.concatenate([es[:, 1], es[:, 0]])
+    deg = np.bincount(src, minlength=len(xy)).astype(float)
+
+    def tri_areas(xy, T):
+        cr = xy[T[:, 1]] - xy[T[:, 0]]
+        cs = xy[T[:, 2]] - xy[T[:, 0]]
+        return cr[:, 0] * cs[:, 1] - cr[:, 1] * cs[:, 0]
+
+    xy_best = xy.copy()
+    for _ in range(8):
+        sx = np.bincount(src, weights=xy[dst, 0], minlength=len(xy))
+        sy = np.bincount(src, weights=xy[dst, 1], minlength=len(xy))
+        new = np.stack([sx, sy], 1) / np.maximum(deg, 1)[:, None]
+        xy2 = xy.copy()
+        xy2[Nb:] = 0.5 * xy[Nb:] + 0.5 * new[Nb:]
+        if tri_areas(xy2, T).min() > 1e-6:
+            xy = xy2
+            xy_best = xy2.copy()
+        else:
+            break
+    xy = xy_best
+
+    # 边翻转优化
+    def flip_pass(Tl, XY):
+        em = {}
+        for fi, t in enumerate(Tl):
+            a, b, c = t
+            for u, v in ((a, b), (b, c), (c, a)):
+                em.setdefault((min(u, v), max(u, v)), []).append(fi)
+        eset = set(em)
+        n = 0
+        for (u, v), fs in em.items():
+            if len(fs) != 2:
+                continue
+            f1, f2 = fs
+            if not (u in Tl[f1] and v in Tl[f1] and u in Tl[f2] and v in Tl[f2]):
+                continue
+            w1 = next(x for x in Tl[f1] if x != u and x != v)
+            w2 = next(x for x in Tl[f2] if x != u and x != v)
+            if w1 == w2 or (min(w1, w2), max(w1, w2)) in eset:
+                continue
+            pu, pv = XY[u], XY[v]
+            s1 = (pv[0]-pu[0])*(XY[w1][1]-pu[1]) - (pv[1]-pu[1])*(XY[w1][0]-pu[0])
+            s2 = (pv[0]-pu[0])*(XY[w2][1]-pu[1]) - (pv[1]-pu[1])*(XY[w2][0]-pu[0])
+            if s1 * s2 >= 0:
+                continue
+            q1, q2 = XY[w1], XY[w2]
+            s3 = (q2[0]-q1[0])*(pu[1]-q1[1]) - (q2[1]-q1[1])*(pu[0]-q1[0])
+            s4 = (q2[0]-q1[0])*(pv[1]-q1[1]) - (q2[1]-q1[1])*(pv[0]-q1[0])
+            if s3 * s4 >= 0:
+                continue
+            if np.hypot(*(XY[w2] - XY[w1])) >= np.hypot(*(XY[v] - XY[u])):
+                continue
+            if s1 > 0:
+                Tl[f1] = [u, w2, w1]
+                Tl[f2] = [v, w1, w2]
+            else:
+                Tl[f1] = [u, w1, w2]
+                Tl[f2] = [v, w2, w1]
+            eset.add((min(w1, w2), max(w1, w2)))
+            n += 1
+        return n
+
+    Tlist = [list(map(int, t)) for t in T]
+    nflips = 0
+    for _p in range(200):
+        k = flip_pass(Tlist, xy)
+        nflips += k
+        if k == 0:
+            break
+    log(f"   边翻转优化: {nflips} 次, 收敛于 {_p+1} 趟")
+
+    # 残余长边细分
+    MAXEDGE = 0.5
+    nsplit = 0
+    for _p in range(6):
+        emap = {}
+        for fi, (a, b, c) in enumerate(Tlist):
+            for u, v in ((a, b), (b, c), (c, a)):
+                emap.setdefault((min(u, v), max(u, v)), []).append(fi)
+        longe = [(u, v) for (u, v) in emap if np.hypot(*(xy[v] - xy[u])) > MAXEDGE]
+        if not longe:
+            break
+        mid = {}
+        newpts = []
+        for u, v in longe:
+            mid[(u, v)] = len(xy) + len(newpts)
+            newpts.append((xy[u] + xy[v]) / 2.0)
+        xy = np.vstack([xy, np.asarray(newpts)])
+        nsplit += len(newpts)
+        newT = []
+        for (a, b, c) in Tlist:
+            mab = mid.get((min(a, b), max(a, b)))
+            mbc = mid.get((min(b, c), max(b, c)))
+            mca = mid.get((min(c, a), max(c, a)))
+            if mab is None and mbc is None and mca is None:
+                newT.append([a, b, c])
+            elif mbc is None and mca is None:
+                newT += [[a, mab, c], [mab, b, c]]
+            elif mab is None and mca is None:
+                newT += [[a, b, mbc], [a, mbc, c]]
+            elif mab is None and mbc is None:
+                newT += [[a, b, mca], [mca, b, c]]
+            elif mca is None:
+                newT += [[a, mab, mbc], [mab, b, mbc], [a, mbc, c]]
+            elif mab is None:
+                newT += [[a, b, mbc], [a, mbc, mca], [mbc, c, mca]]
+            elif mbc is None:
+                newT += [[a, mab, mca], [mab, b, c], [mab, c, mca]]
+            else:
+                newT += [[a, mab, mca], [mab, b, mbc], [mca, mbc, c], [mab, mbc, mca]]
+        Tlist = newT
+    T = np.asarray(Tlist, np.int64)
+    log(f"   残余长边细分(>{MAXEDGE}): 新增 {nsplit} 中点, 总三角形 {len(T)}")
+
+    cr = xy[T[:, 1]] - xy[T[:, 0]]
+    cs = xy[T[:, 2]] - xy[T[:, 0]]
+    area = cr[:, 0] * cs[:, 1] - cr[:, 1] * cs[:, 0]
+    if area.min() <= 0:
+        raise SystemExit(f"三角形定向异常 (min area {area.min():.2e})")
+    log(f"   实际插入 {extra} 点, 总三角形 {len(T)}, 最小有向面积 {area.min():.5f} (>0 = 无翻转)")
+
+    # 计算 z 坐标
+    zf = bilinear(hf, xy)
+    zb = bilinear(hb, xy)
+    zb = np.minimum(zb, zf - 0.15)
+    zb = np.maximum(zb, zf - 50)
+
+    Pf = np.column_stack([xy, zf])
+    Pb = np.column_stack([xy, zb])
+
+    return Pf, Pb, T, xy
+
+# ======================================================================
+# 步骤 7: 组装实体 (前/后表面 + 侧墙)
+# ======================================================================
+def assemble_mesh(Pf, Pb, T):
+    """组装填充实体: 前表面 + 后表面 + 侧墙"""
+    log("== 7. 组装填充实体 ==")
+    nV = len(Pf)
+    faces = []
+
+    # 前表面 (法向 +z, 向外)
+    for a, b, c in T:
+        faces.append((a, b, c))
+
+    # 后表面 (翻转, 法向 -z, 向外)
+    for a, b, c in T:
+        faces.append((a + nV, c + nV, b + nV))
+
+    # 侧墙: 提取边界环
+    dired = {}
+    unded = {}
+    for a, b, c in T:
+        for u, v in ((a, b), (b, c), (c, a)):
+            dired[(u, v)] = dired.get((u, v), 0) + 1
+    for (u, v) in dired:
+        unded[(min(u, v), max(u, v))] = unded.get((min(u, v), max(u, v)), 0) + 1
+
+    bnext = {}
+    for (u, v) in dired:
+        if unded[(min(u, v), max(u, v))] == 1:
+            bnext[u] = v
+
+    ring = [next(iter(bnext))]
+    while bnext[ring[-1]] != ring[0]:
+        ring.append(bnext[ring[-1]])
+
+    Nr = len(ring)
+    for i in range(Nr):
+        u, v = ring[i], ring[(i + 1) % Nr]
+        faces.append((u, v + nV, v))            # (F_i, B_{i+1}, F_{i+1})
+        faces.append((u, u + nV, v + nV))       # (F_i, B_i, B_{i+1})
+
+    Ff = np.asarray(faces, np.int64)
+    Pall = np.vstack([Pf, Pb])
+
+    return Pall, Ff, nV
+
+
+# ======================================================================
+# 步骤 8: 校验
+# ======================================================================
+def validate_mesh(Pall, Ff, Pf, Pb, T, V, F, hf, hb, gz1f, frame_band,
+                  wall_zone, bzs, have_b, real_f, fz, R, xy):
+    """对生成的网格进行各项校验"""
+    log("== 8. 校验 ==")
+
+    # 定向校验
+    vfill = signed_volume(Pall, Ff)
+    vorig = signed_volume(V, F)
+    if vfill * vorig < 0:
+        Ff = Ff[:, [0, 2, 1]]
+        vfill = -vfill
+
+    # 期望体积
+    area = np.cross(Pf[T[:, 1]] - Pf[T[:, 0]], Pf[T[:, 2]] - Pf[T[:, 0]])[:, 2]
+    exp_vol = float(np.sum(0.5 * np.abs(area) *
+                           ((Pf[T[:, 0], 2] + Pf[T[:, 1], 2] + Pf[T[:, 2], 2]
+                             - Pb[T[:, 0], 2] - Pb[T[:, 1], 2] - Pb[T[:, 2], 2]) / 3.0)))
+    log(f"   填充实体体积 {vfill:.2f} (期望 {exp_vol:.2f}), 原模型 {vorig:.0f}, 定向一致")
+    if abs(vfill - exp_vol) > 0.02 * abs(exp_vol) + 1e-6:
+        raise SystemExit("体积校验失败: 面片定向不一致")
+
+    # 水密性
+    e2 = np.sort(np.concatenate([Ff[:, [0, 1]], Ff[:, [1, 2]], Ff[:, [2, 0]]]), axis=1)
+    ue, cnt = np.unique(e2, axis=0, return_counts=True)
+    de = np.concatenate([Ff[:, [0, 1]], Ff[:, [1, 2]], Ff[:, [2, 0]]])
+    ude, udcnt = np.unique(de, axis=0, return_counts=True)
+    log(f"   水密性: 非流形边(计数!=2) {int((cnt != 2).sum())} / {len(ue)}, "
+        f"方向冲突边 {int((udcnt != 1).sum())} (两者都应为 0)")
+
+    # 镜片光滑度
+    nv = np.cross(Pall[Ff[:, 1]] - Pall[Ff[:, 0]], Pall[Ff[:, 2]] - Pall[Ff[:, 0]])
+    nv = nv / np.linalg.norm(nv, axis=1, keepdims=True)
+    fcen = Pall[Ff].mean(1)
+    emap2 = {}
+    for fi, (a, b, c) in enumerate(Ff):
+        for u, v in ((a, b), (b, c), (c, a)):
+            emap2.setdefault((min(u, v), max(u, v)), []).append(fi)
+    lang, lloc = [], []
+    for (u, v), fs in emap2.items():
+        if len(fs) == 2 and nv[fs[0], 2] > 0.5 and nv[fs[1], 2] > 0.5:
+            m = fcen[fs[0]]
+            if 2.2 < abs(m[0]) < 6.3 and 12.5 < m[1] < 16.2:
+                lang.append(np.degrees(np.arccos(np.clip(nv[fs[0]] @ nv[fs[1]], -1, 1))))
+                lloc.append(m)
+    lang = np.asarray(lang)
+    if len(lang):
+        k = np.argmax(lang)
+        log(f"   镜片光滑度(相邻面二面角): 中位 {np.median(lang):.2f}°, "
+            f"p90 {np.percentile(lang, 90):.2f}°, "
+            f"max {lang[k]:.2f}° @ ({lloc[k][0]:.1f},{lloc[k][1]:.1f}) (中位应<2°)")
+
+    # 嵌入校验
+    gmargin = (gz1f - hf)[R & frame_band & wall_zone]
+    log(f"   前表面低于本地框面(墙区): 最小余量 {np.nanmin(gmargin):.3f} (应>0)")
+    gmargin2 = (gz1f - hf)[R & frame_band]
+    log(f"   前表面低于本地框面(内部允许贴齐): 最小余量 {np.nanmin(gmargin2):.3f} (应>-0.10)")
+    bmargin = (hb - bzs)[R & have_b]
+    log(f"   后表面高于最后表面: 最小余量 {np.nanmin(bmargin):.3f} (应>0)")
+    embed = (fz - hb)[R & real_f]
+    log(f"   底面嵌入真实脸面: 最小 {np.nanmin(embed):.3f} (应>-0.05, 负=缝隙), "
+        f"中位 {np.nanmedian(embed):.2f}")
+    inR = inside_mask(xy, R)
+    log(f"   圆盘顶点落在 R 内比例 {inR.mean()*100:.1f}%")
+
+    return Ff, vfill
+
+
+# ======================================================================
+# 步骤 9: 输出
+# ======================================================================
+def output_mesh(Pall, Ff, V, F):
+    """输出填充网格和合并后的模型"""
+    log("== 9. 输出 ==")
+
+    # 输出填充网格
+    with open(OUT_FILL, "w") as f:
+        f.write("# fill mesh between glasses and face\n")
+        f.write("o fill\n")
+        for p in Pall:
+            f.write(f"v {p[0]:.5f} {p[1]:.5f} {p[2]:.5f}\n")
+        for a, b, c in Ff:
+            f.write(f"f {a+1} {b+1} {c+1}\n")
+    log(f"   {OUT_FILL}: {len(Pall)} 顶点, {len(Ff)} 面")
+
+    # 输出合并模型
+    with open(IN_OBJ) as f:
+        orig = f.read()
+    with open(OUT_MERGED, "w") as f:
+        f.write(orig.rstrip("\n") + "\n")
+        f.write("o fill\n")
+        off = len(V)
+        for p in Pall:
+            f.write(f"v {p[0]:.5f} {p[1]:.5f} {p[2]:.5f}\n")
+        for a, b, c in Ff:
+            f.write(f"f {a+1+off} {b+1+off} {c+1+off}\n")
+    log(f"   {OUT_MERGED}: 原模型 + 填充 (顶点偏移 {off})")
+
+
+# ======================================================================
+# 步骤 10: 生成预览图
+# ======================================================================
+def generate_preview(gz1, hf, hb, R, B):
+    """生成前视图深度预览"""
+    log("== 10. 生成预览图 ==")
+
+    # 合成前表面深度图
+    front = np.where(np.isfinite(gz1) & (gz1 > -1e30), gz1, np.nan)
+    mix = np.where(R & np.isfinite(hf),
+                   np.where(np.isfinite(front), np.maximum(front, hf), hf),
+                   front)
+    front = mix
+
+    panels = []
+    span = (4, 12)
+
+    def to_img(Grd, span):
+        G = np.where(np.isfinite(Grd), Grd, span[0])
+        t = np.clip((G - span[0]) / (span[1] - span[0]), 0, 1)
+        return (t * 255).astype(np.uint8)
+
+    for name, Grd in (("front-depth", front),
+                      ("fill-front", np.where(R, hf, np.nan)),
+                      ("thickness", np.where(R, hf - hb, np.nan))):
+        im = Image.fromarray(to_img(np.flipud(Grd),
+                                    span if "thick" not in name else (0, 4)))
+        im = im.resize((W * 4, H * 4), Image.NEAREST)
+        d = ImageDraw.Draw(im)
+        pts = [((b[0] - X0) / RES * 4, (Y1 - b[1]) / RES * 4) for b in B]
+        d.line(pts + pts[:1], fill=255, width=2)
+        panels.append((name, im))
+
+    cw = sum(p.width for _, p in panels) + 20
+    ch = max(p.height for _, p in panels)
+    combo = Image.new("L", (cw, ch), 60)
+    xoff = 0
+    for name, p in panels:
+        combo.paste(p, (xoff, 0))
+        xoff += p.width + 10
+    combo.save(OUT_PREVIEW)
+    log(f"   {OUT_PREVIEW}: 3 面板(前深度/填充前表面/厚度), 红线=平滑边界")
+
+
+# ======================================================================
+# 主流程
+# ======================================================================
+if __name__ == "__main__":
+    # 步骤 1: 加载模型
+    V, F, face_g = load_and_classify()
+
+    # 步骤 2: 栅格化
+    gz1, gz2, fz, bz, have_g1, have_g2, have_f, have_b, g1f, W, H = \
+        rasterize(V, F, face_g)
+
+    # 步骤 3: 构建足迹
+    mask, R = build_footprint(g1f)
+
+    # 步骤 4: 构造高度场
+    hf, hb, frame_band, wall_zone, gz1f, bzs, real_f, fz = \
+        build_height_fields(gz1, gz2, fz, bz, have_g1, have_f, have_b,
+                           g1f, mask, R)
+
+    # 步骤 5: 边界提取与平滑
+    B, loop = extract_and_smooth_boundary(R, mask, g1f)
+
+    # 步骤 6: 三角剖分
+    Pf, Pb, T, xy = triangulate_fill(B, R, hf, hb)
+
+    # 步骤 7: 组装实体
+    Pall, Ff, nV = assemble_mesh(Pf, Pb, T)
+
+    # 步骤 8: 校验
+    Ff, vfill = validate_mesh(Pall, Ff, Pf, Pb, T, V, F, hf, hb,
+                              gz1f, frame_band, wall_zone, bzs,
+                              have_b, real_f, fz, R, xy)
+
+    # 步骤 9: 输出
+    output_mesh(Pall, Ff, V, F)
+
+    # 步骤 10: 预览图
+    generate_preview(gz1, hf, hb, R, B)
+
+    log("\n== 完成 ==")
+    with open("fill_report.txt", "w") as f:
+        f.write("\n".join(report) + "\n")
+    print("报告已写入 fill_report.txt")
\ No newline at end of file