Fast version of get_innner_win_inds_slow Args: win_inds indicates which windows a voxel belongs to. Voxels share a windo

Fast version of get_innner_win_inds_slow Args: win_inds indicates which windows a voxel belongs to. Voxels share a windo

Fast version of get_innner_win_inds_slow Args: win_inds indicates which windows a voxel belongs to. Voxels share a windo

Fast version of get_innner_win_inds_slow Args: win_inds indicates which windows a voxel belongs to. Voxels share a windo

Fast version of get_innner_win_inds_slow Args: win_inds indicates which windows a voxel belongs to. Voxels share a windo

Fast version of get_innner_win_inds_slow Args: win_inds indicates which windows a voxel belongs to. Voxels share a windo

Calculate loss of voting module. Args: seed_points (torch.Tensor): Coordinate of the seed points. vote_points (torch.

Create boolean mask by actually number of a padded tensor. Args: actual_num (torch.Tensor): Actual number of points in each voxel.

Get points according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. dtype (t

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Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Args: voxel_feat: shape=[N, C], N is the voxel num in the batch. coors: shape=[N, 4], [b, z, y, x] Returns:

Check whether the points are in the given range. Args: point_range (list | torch.Tensor): The range of point in o

Inference with slide/whole style. Args: points (torch.Tensor): Input points of shape [B, N, 3+C]. img_metas (list[dic

Inference point cloud with the multi-modality detector. Args: model (nn.Module): The loaded detector. pcd (str): Point cloud file

Initialize weights of shared MLP layers.

Prepare data related to Lyft dataset. Related data consists of '.pkl' files recording basic infos, and 2D annotations. Although the groun

Map voxel features to its corresponding points. Args: pts_coors (torch.Tensor): Voxel coordinate of each point. voxel

Map the centers of voxels to its corresponding points. Args: pts_coors (torch.Tensor): The coordinates of each points, shape

Multi-class NMS for box head. Note: This function has large overlap with the `box3d_multiclass_nms` implemented in `m

Nms function with gpu implementation. Args: boxes (torch.Tensor): Input boxes with the shape of [N, 5] ([x1, y1, x2, y2, ry])

Random rotate or remove each groundtruth independently. use kitti viewer to test this function points_transform_ Args: gt_boxes (np.n

Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. senso

Convert the output to the box class in the nuScenes. Args: detection (dict): Detection results. - boxes_3d (:obj:`BaseInstan

Extract the axis aligned box from a pcl or batch of pcls Args: points: Nx3 points or BxNx3 output is 6 dim: xyz pos of center and

Obtain image features using points. Args: img_meta (dict): Meta info. img_features (torch.Tensor): 1 x C x H x W image features.

Check points is in 3d convex polygons. Args: points (np.ndarray): Input points with shape of (num_points, 3). polygon_surfaces (n

Apply transforms to points and box centers. Args: points (np.ndarray): Input points. centers (np.ndarray): Input box centers.

Get the intersection of the convex hull of the reprojected bbox corners and the image canvas, return None if no intersection. Args: c

Flip 3D data randomly. Args: input_dict (dict): Result dict from loading pipeline. direction (str): Flip direction. D

Input is NxC, output is num_samplexC

Remove the points in the sampled bounding boxes. Args: points (:obj:`BasePoints`): Input point cloud array. boxes (np

Rotated box iou running in gpu. 500x faster than cpu version (take 5ms in one example with numba.cuda code). convert from [this project]( http

Rotation 2d points based on origin point clockwise when angle positive. Args: points (np.ndarray): Points to be rotated with shape \

Semantic Segmentation Evaluation. Evaluate the result of the Semantic Segmentation. Args: gt_labels (list[torch.Tensor]): Ground tr

Visualize the points cloud. Args: save_path (str): path to save image. Default: None.

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory of

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory of

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory of

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory of

Test model with single gpu. This method tests model with single gpu and gives the 'show' option. By setting ``show=True``, it saves the visua

Generate grid anchors of a single level feature map. This function is usually called by method ``self.grid_anchors``. Args:

Apply hard voxelization to points.

Call function to apply noise to each ground truth in the scene. Args: input_dict (dict): Result dict from loading pipeline.

Call function to load multiple types annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. R

Call function to collect keys in results. The keys in ``meta_keys`` will be converted to :obj:`mmcv.DataContainer`. Args: