import open3d as o3d import numpy as np import os def align_scene_to_z_up(pcd, save_pcd_path=None, save_transform_path=None, visualize=False, fit_ground=True): """ 将点云扫描转换为Z轴向上对齐，并尝试将墙壁对齐到X-Y平面 参数: points (np.ndarray): (N, 3) 点云数据 visualize (bool): 是否可视化结果 返回: aligned_points (np.ndarray): 对齐后的点云 transform_matrix (np.ndarray): 应用的4x4变换矩阵 """ points = np.asarray(pcd.points) centroid = np.mean(points, axis=0) distance_threshold = 0.02 # 动态距离阈值 R_ground = np.eye(3) # Initialize ground rotation matrix if fit_ground: plane_model, ground_inliers = pcd.segment_plane( distance_threshold=distance_threshold, ransac_n=3, num_iterations=1000 ) [a, b, c, d] = plane_model ground_normal = np.array([a, b, c]) # print('ground_normal:', ground_normal) # 可视化地面点云 ground_cloud = pcd.select_by_index(ground_inliers) ground_cloud.paint_uniform_color([1, 0, 0]) # 红色表示地面 if visualize: o3d.visualization.draw_geometries([ground_cloud]) # 3. 计算旋转矩阵使地面法向量对齐到Z轴 z_axis = np.array([0, 0, 1]) ground_normal = ground_normal / np.linalg.norm(ground_normal) # 计算旋转轴和角度 rotation_axis = np.cross(ground_normal, z_axis) if np.linalg.norm(rotation_axis) < 1e-6: rotation_axis = np.array([0, 1, 0]) # 避免零向量 else: rotation_axis = rotation_axis / np.linalg.norm(rotation_axis) cos_theta = np.dot(ground_normal, z_axis) angle = np.arccos(np.clip(cos_theta, -1.0, 1.0)) # 使用罗德里格斯公式计算旋转矩阵 K = np.array([ [0, -rotation_axis[2], rotation_axis[1]], [rotation_axis[2], 0, -rotation_axis[0]], [-rotation_axis[1], rotation_axis[0], 0] ]) R_ground = np.eye(3) + np.sin(angle) * K + (1 - np.cos(angle)) * (K @ K) # [Alternate] concise method # from scipy.spatial.transform import Rotation as R # R_ground = R.from_rotvec(rotation_axis * angle).as_matrix() # 4. 应用地面旋转 centered_points = points - centroid ground_rotated = (R_ground @ centered_points.T).T # 5. 检测墙壁平面（垂直平面） all_indices = np.arange(len(points)) non_ground_indices = np.setdiff1d(all_indices, ground_inliers) non_ground_points = ground_rotated[non_ground_indices] # 创建临时点云用于墙壁检测 temp_pcd = o3d.geometry.PointCloud() temp_pcd.points = o3d.utility.Vector3dVector(non_ground_points) remaining_pcd = temp_pcd else: # already z up remaining_pcd = pcd wall_planes = [] wall_directions = [] max_points = 0 best_direction = None R_walls = np.eye(3) # 检测多个墙壁平面 for _ in range(6): if len(remaining_pcd.points) < 100: break plane_model, inliers = remaining_pcd.segment_plane( distance_threshold=distance_threshold, ransac_n=3, num_iterations=1000 ) wall_cloud = remaining_pcd.select_by_index(inliers) wall_cloud.paint_uniform_color([1, 0, 0]) # 红色表示地面 if visualize: o3d.visualization.draw_geometries([wall_cloud]) [a, b, c, d] = plane_model normal = np.array([a, b, c]) # 检查是否为垂直平面（法向量的Z分量接近0） if abs(normal[2]) < 0.1 and np.linalg.norm(normal[:2]) > 0.5: # 提取水平方向 horizontal_dir = normal[:2] / np.linalg.norm(normal[:2]) wall_directions = horizontal_dir # print('wall direction',normal) if max_points < len(inliers): max_points = len(inliers) # print(max_points) best_direction = wall_directions # 计算最佳方向的旋转角度 angle = np.arctan2(best_direction[1], best_direction[0]) # 创建最终的旋转矩阵 R_walls = np.array([ [np.cos(-angle), -np.sin(-angle), 0], [np.sin(-angle), np.cos(-angle), 0], [0, 0, 1] ]) remaining_pcd = remaining_pcd.select_by_index(inliers, invert=True) # 8. 创建变换矩阵（旋转 + 平移） if fit_ground: # Compose the transformations: first ground rotation, then wall rotation combined_rotation = R_walls @ R_ground transform_matrix = np.eye(4) transform_matrix[:3, :3] = combined_rotation transform_matrix[:3, 3] = -combined_rotation @ centroid else: # Only wall rotation transform_matrix = np.eye(4) transform_matrix[:3, :3] = R_walls transform_matrix[:3, 3] = -R_walls @ centroid # 平移使中心到原点 # 9. 应用变换 aligned_points = (transform_matrix[:3, :3] @ points.T + transform_matrix[:3, 3:4]).T # 10. 创建对齐后的点云 aligned_pcd = o3d.geometry.PointCloud() aligned_pcd.points = o3d.utility.Vector3dVector(aligned_points) if pcd.colors: aligned_pcd.colors = pcd.colors # 11. 保存结果 if save_pcd_path: o3d.io.write_point_cloud(save_pcd_path, aligned_pcd) if save_transform_path: np.savetxt(save_transform_path, transform_matrix, fmt='%.8f') return aligned_pcd, transform_matrix """创建4x4变换矩阵""" transform = np.eye(4) transform[:3, :3] = rotation_matrix return transform def visualize_alignment(pcd_orig, pcd_aligned): """可视化原始点云和对齐后的点云""" pcd_orig.paint_uniform_color([1, 0, 0]) # 红色为原始点云 pcd_aligned.paint_uniform_color([0, 1, 0]) # 绿色为对齐后点云 # 创建坐标系 coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1.0) o3d.visualization.draw_geometries([pcd_orig, pcd_aligned, coord_frame]) npy_dir = '/media/vivo/vivo/Datasets/ScanNetpp/ScanNetpp_preprocessed/train/' scene_names = os.listdir(npy_dir) for scene_name in scene_names: if scene_name.endswith('.zip'): continue print(scene_name) scene_dir = os.path.join(npy_dir, scene_name) npy_path = os.path.join(scene_dir, 'coord.npy') points = np.load(npy_path) # (N, 3) 或 (N, 6) # 创建点云对象 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(points[:, :3]) if points.shape[1] == 6: pcd.colors = o3d.utility.Vector3dVector(points[:, 3:6]) # 如果包含 RGB # 执行对齐 transformed_pcd, transform = align_scene_to_z_up( pcd, # save_transform_path=os.path.join(scene_dir, "transform.txt"), visualize=False, fit_ground=False ) aligned_points = np.asarray(transformed_pcd.points) # scale point cloud # min_z = np.min(aligned_points[:, 2]) # max_z = np.max(aligned_points[:, 2]) # height = max_z - min_z # print(f"Original height: {height}") # estimated_height = 2.5 # scale = estimated_height / height # print(f"Scale factor: {scale}") # aligned_points_scaled = aligned_points * scale transformed_pcd.points = o3d.utility.Vector3dVector(aligned_points) # 保存为对齐后的 PLY 文件 # aligned_ply_path = os.path.join(scene_dir, scene_name + ".ply") # o3d.io.write_point_cloud(aligned_ply_path, transformed_pcd) # 也可以保存为对齐后的 npy np.save(os.path.join(scene_dir, "coord_align.npy"), aligned_points)