File size: 3,647 Bytes
90ca518 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | """点云距离与法向一致性指标(倒角 L1/L2、豪斯多夫、|NC| / NC)。
modules.pipeline 与 scripts.eval 共用同一份实现, 保证推理内打印的指标与离线评测同口径。
"""
import numpy as np
from scipy.spatial import cKDTree
def calculate_distances_numpy(pcd1, pcd2):
"""
[已修改]
使用 NumPy 高效计算 L2倒角距离、L1倒角距离 和 豪斯多夫距离。
参数:
pcd1: np.ndarray, 形状为 (N, 3)，第一个点云。
pcd2: np.ndarray, 形状为 (M, 3)，第二个点云。
返回:
(float, float, float): (L2倒角距离, 豪斯多夫距离, L1倒角距离)
"""
# --- 计算所有点对之间的差异 ---
pcd1_expanded = pcd1[:, np.newaxis, :]
pcd2_expanded = pcd2[np.newaxis, :, :]
diff = pcd1_expanded - pcd2_expanded
# --- L2 (欧氏) 距离计算 ---
dist_matrix_sq = np.sum(diff**2, axis=2)
# --- L2 倒角距离 (Chamfer Distance L2) ---
min_dists_1_to_2_l2 = np.sqrt(np.min(dist_matrix_sq, axis=1))
min_dists_2_to_1_l2 = np.sqrt(np.min(dist_matrix_sq, axis=0))
chamfer_dist_l2 = np.mean(min_dists_1_to_2_l2) + np.mean(min_dists_2_to_1_l2)
# --- L2 豪斯多夫距离 (Hausdorff Distance) ---
hausdorff_dist = max(np.max(min_dists_1_to_2_l2), np.max(min_dists_2_to_1_l2))
# --- L1 (曼哈顿) 距离计算 ---
dist_matrix_l1 = np.sum(np.abs(diff), axis=2)
# --- L1 倒角距离 (Chamfer Distance L1) ---
min_dists_1_to_2_l1 = np.min(dist_matrix_l1, axis=1)
min_dists_2_to_1_l1 = np.min(dist_matrix_l1, axis=0)
chamfer_dist_l1 = np.mean(min_dists_1_to_2_l1) + np.mean(min_dists_2_to_1_l1)
return chamfer_dist_l2, hausdorff_dist, chamfer_dist_l1
def calculate_normal_consistency_score(pcd_gt, normal_gt, pcd_pred, normal_pred):
"""
【修改版】根据提供的公式计算法向量一致性得分 (Normal Consistency Score)。
返回两个值：
1. |NC| (绝对值 NC): np.mean( |n1·n2| )
2. NC (真实值 NC): np.mean( n1·n2 )
"""
def _calculate_one_way_score(source_pcd, source_normals, target_pcd, target_normals):
"""
辅助函数：计算从 source 到 target 的单向 NC 得分。
返回 (mean_abs_dot, mean_real_dot)
"""
# 1. 计算 source 到 target 的所有点对的距离矩阵
diff = source_pcd[:, np.newaxis, :] - target_pcd[np.newaxis, :, :]
dist_sq = np.sum(diff**2, axis=2)
# 2. 为每个 source 点找到 target 中的最近邻点的索引
indices = np.argmin(dist_sq, axis=1)
# 3. 获取这些最近邻点对应的法向量
corresponding_normals = target_normals[indices]
# 4. 计算 source 法向量与对应 target 法向量的点积
dot_products = np.einsum('ij,ij->i', source_normals, corresponding_normals)
# 5. [修改] 返回点积绝对值的平均值 和 真实点积的平均值
return np.mean(np.abs(dot_products)), np.mean(dot_products)
# --- Part 1: 计算从 GT -> Pred 的得分 ---
score_gt_to_pred_abs, score_gt_to_pred_real = _calculate_one_way_score(pcd_gt, normal_gt, pcd_pred, normal_pred)
# --- Part 2: 计算从 Pred -> GT 的得分 ---
score_pred_to_gt_abs, score_pred_to_gt_real = _calculate_one_way_score(pcd_pred, normal_pred, pcd_gt, normal_gt)
# --- Final Score: 结合两部分 ---
final_score_abs = (score_gt_to_pred_abs + score_pred_to_gt_abs) / 2.0
final_score_real = (score_gt_to_pred_real + score_pred_to_gt_real) / 2.0
# [修改] 返回两个分数
return final_score_abs, final_score_real
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