Zexian Huang zexhuang
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| import neuralnet_pytorch as nnt | |
| import torch as T | |
| from torch_scatter import scatter_add | |
| def pointcloud2voxel_fast(pc: T.Tensor, voxel_size: int, grid_size=1., filter_outlier=True): | |
| b, n, _ = pc.shape | |
| half_size = grid_size / 2. | |
| valid = (pc >= -half_size) & (pc <= half_size) | |
| valid = T.all(valid, 2) |
Ajk4
/ point_cloud_to_depth_map.py
Last active
August 7, 2024 16:12
Convert point cloud to depth map
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| import numpy as np | |
| def pointcloud_to_depth_map(pointcloud: np.ndarray, theta_res=150, phi_res=32, max_depth=50, phi_min_degrees=60, | |
| phi_max_degrees=100) -> np.ndarray: | |
| """ | |
| All params are set so they match default carla lidar settings | |
| """ | |
| assert pointcloud.shape[1] == 3, 'Must have (N, 3) shape' | |
| assert len(pointcloud.shape) == 2, 'Must have (N, 3) shape' |