feat: 算法升级、整车配准集成与性能分析文档

算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3
适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新
后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动
前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复
文档: 新增5份算法性能分析报告

alg/平整度、平面度、轮廓测量v1.5/preprocessing/curvature.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import copy
+import numpy as np
+import open3d as o3d
+
+
+class CurvatureEstimator:
+    """Estimate local point-cloud curvature using neighborhood covariance."""
+
+    def compute(self, pointcloud: o3d.geometry.PointCloud, radius: float = 2.0, max_nn: int = 30) -> np.ndarray:
+        pointcloud.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=radius, max_nn=max_nn))
+        points = np.asarray(pointcloud.points)
+        kdtree = o3d.geometry.KDTreeFlann(pointcloud)
+        curvatures = np.zeros(len(points), dtype=np.float64)
+
+        for i, point in enumerate(points):
+            _, indices, _ = kdtree.search_hybrid_vector_3d(point, radius, max_nn)
+            if len(indices) < 3:
+                curvatures[i] = 1.0
+                continue
+
+            neighbors = points[indices]
+            cov = np.cov((neighbors - neighbors.mean(axis=0)).T)
+            eigenvalues = np.linalg.eigvalsh(cov)
+            denom = eigenvalues.sum()
+            curvatures[i] = float(eigenvalues[0] / denom) if denom > 0 else 0.0
+
+        return curvatures
+
+    def colorize(self, pointcloud: o3d.geometry.PointCloud, curvatures: np.ndarray, color_scale: float = 3.0) -> o3d.geometry.PointCloud:
+        pcd = copy.deepcopy(pointcloud)
+        normalized = curvatures / (np.max(curvatures) + 1e-12)
+        normalized = np.clip(normalized * color_scale, 0, 1)
+        colors = np.zeros((len(normalized), 3))
+        colors[:, 0] = normalized
+        colors[:, 1] = 1 - normalized
+        pcd.colors = o3d.utility.Vector3dVector(colors)
+        return pcd