Learn2Splat / optgs /visualization /export_point_cloud.py
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import numpy as np
import os
import torch
try:
import open3d as o3d
except:
pass
def export_to_point_cloud(point_cloud, colors,
save_path='point_cloud.ply',
denoise_cloud=False,
denoise_nb_points=10,
denoise_radius=0.03,
):
# point_cloud: [N, 3]
# colors: [N, 3]
# point_cloud = point_cloud / 10.
point_cloud = point_cloud - np.median(point_cloud, axis=0, keepdims=True)
# Ensure colors are in the range [0, 1]
colors = np.clip(colors, 0, 1)
# Create an Open3D PointCloud object
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(point_cloud)
pcd.colors = o3d.utility.Vector3dVector(colors)
save_dir = os.path.dirname(save_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Export the point cloud to a PLY file
o3d.io.write_point_cloud(save_path, pcd)
if denoise_cloud:
print("denoise point cloud...")
cl, ind = pcd.remove_radius_outlier(nb_points=denoise_nb_points, radius=denoise_radius)
inlier_cloud = pcd.select_by_index(ind)
o3d.io.write_point_cloud(save_path[:-4] + '_denoise.ply', inlier_cloud)
def transform_points(world_points, cam_to_world):
"""
Transforms world 3D points to camera coordinates.
Args:
world_points (torch.Tensor): Nx3 tensor of 3D points in world coordinates.
cam_to_world (torch.Tensor): 4x4 tensor of camera-to-world extrinsics.
Returns:
torch.Tensor: Nx3 tensor of 3D points in camera coordinates.
"""
# Convert world points to homogeneous coordinates (Nx4)
N = world_points.shape[0]
ones = torch.ones((N, 1), device=world_points.device)
world_points_h = torch.cat([world_points, ones], dim=1) # Nx4
# Compute the inverse of the extrinsics (world-to-camera transformation)
world_to_cam = torch.inverse(cam_to_world)
# Apply transformation
camera_points_h = (world_to_cam @ world_points_h.T).T # Nx4
# Convert back to 3D coordinates (drop the homogeneous coordinate)
camera_points = camera_points_h[:, :3] # Nx3
return camera_points