将点特征 转换为 voxel 特征

https://github.com/skyhehe123/VoxelNet-pytorch/blob/master/data/kitti.py

【Python】np.unique() 介绍与使用

self.T ： # maxiumum number of points per voxel

    def preprocess(self, lidar):

        # shuffling the points
        np.random.shuffle(lidar)

        voxel_coords = ((lidar[:, :3] - np.array([self.xrange[0], self.yrange[0], self.zrange[0]])) / (
                        self.vw, self.vh, self.vd)).astype(np.int32)

        # convert to  (D, H, W)
        voxel_coords = voxel_coords[:,[2,1,0]]
        voxel_coords, inv_ind, voxel_counts = np.unique(voxel_coords, axis=0, \
                                                  return_inverse=True, return_counts=True)

        voxel_features = []

        for i in range(len(voxel_coords)):
            voxel = np.zeros((self.T, 7), dtype=np.float32)
            pts = lidar[inv_ind == i] # 落到同一个voxel上的 点
            if voxel_counts[i] > self.T:
                pts = pts[:self.T, :]
                voxel_counts[i] = self.T
            # augment the points
            voxel[:pts.shape[0], :] = np.concatenate((pts, pts[:, :3] - np.mean(pts[:, :3], 0)), axis=1)
            voxel_features.append(voxel)
        return np.array(voxel_features), voxel_coords

输入，输出解释