def transform_grid(self, voxel_grid, grid_to_lidar, lidar_to_cam,
cam_to_img):
"""
Transforms voxel sampling grid into frustum sampling grid
Args:
voxel_grid [torch.Tensor(B, X, Y, Z, 3)]: Voxel sampling grid
grid_to_lidar [torch.Tensor(4, 4)]: Voxel grid to LiDAR unprojection matrix
lidar_to_cam [torch.Tensor(B, 4, 4)]: LiDAR to camera frame transformation
cam_to_img [torch.Tensor(B, 3, 4)]: Camera projection matrix
Returns:
frustum_grid [torch.Tensor(B, X, Y, Z, 3)]: Frustum sampling grid
"""
# B是相机数目
B = lidar_to_cam.shape[0]
# Create transformation matricies
V_G = grid_to_lidar # Voxel Grid -> LiDAR (4, 4)
C_V = lidar_to_cam # LiDAR -> Camera (B, 4, 4)
I_C = cam_to_img # Camera -> Image (B, 3, 4)
trans = C_V @ V_G # grid转到lidar实际坐标，再转到相机坐标再转到像素。主要是为了grid和像素对应。
# Reshape to match dimensions
trans = trans.reshape(B, 1, 1, 4, 4)
voxel_grid = voxel_grid.repeat_interleave(repeats=B, dim=0)
# Transform to camera frame
#camera_grid shape: B X Y Z 3
camera_grid = kornia.transform_points(trans_01=trans,
points_1=voxel_grid)
# Project to image
I_C = I_C.reshape(B, 1, 1, 3, 4)
# image_grid shape: B X Y Z 2; image_depth B X Y Z 1
image_grid, image_depths = transform_utils.project_to_image(
project=I_C, points=camera_grid)
# Convert depths to depth bins
# Image_depths.shape: B X Y Z 1 落在哪个bin
image_depths = depth_utils.bin_depths(depth_map=image_depths,
**self.disc_cfg)
# Stack to form frustum grid
image_depths = image_depths.unsqueeze(-1)
# frustum_grid = B X Y Z 3
frustum_grid = torch.cat((image_grid, image_depths), dim=-1)
return frustum_grid
def transform_grid(self, voxel_grid, grid_to_lidar, lidar_to_cam,
cam_to_img):
"""
Transforms voxel sampling grid into frustum sampling grid
Args:
grid: (B, X, Y, Z, 3), Voxel sampling grid
grid_to_lidar: (4, 4), Voxel grid to LiDAR unprojection matrix
lidar_to_cam: (B, 4, 4), LiDAR to camera frame transformation
cam_to_img: (B, 3, 4), Camera projection matrix
Returns:
frustum_grid: (B, X, Y, Z, 3), Frustum sampling grid
"""
B = lidar_to_cam.shape[0]
# Create transformation matricies
V_G = grid_to_lidar # Voxel Grid -> LiDAR (4, 4)
C_V = lidar_to_cam # LiDAR -> Camera (B, 4, 4)
I_C = cam_to_img # Camera -> Image (B, 3, 4)
trans = C_V @ V_G
# Reshape to match dimensions
trans = trans.reshape(B, 1, 1, 4, 4)
voxel_grid = voxel_grid.repeat_interleave(repeats=B, dim=0)
# Transform to camera frame
camera_grid = kornia.transform_points(trans_01=trans,
points_1=voxel_grid)
# Project to image
I_C = I_C.reshape(B, 1, 1, 3, 4)
image_grid, image_depths = transform_utils.project_to_image(
project=I_C, points=camera_grid)
# Convert depths to depth bins
image_depths = transform_utils.bin_depths(depth_map=image_depths,
**self.disc_cfg)
# Stack to form frustum grid
image_depths = image_depths.unsqueeze(-1)
frustum_grid = torch.cat((image_grid, image_depths), dim=-1)
return frustum_grid