Rotate boxes with points (optional) with the given angle or \ rotation matrix. Args: angles (float | torch.Tensor | np.nd

Rotate boxes with points (optional) with the given angle or \ rotation matrix. Args: angle (float | torch.Tensor | np.nda

Split predicted features to specific parts. Args: cls_preds (torch.Tensor): Class predicted features to split. reg_pr

Initialize conv layers as a prediction branch.

Assign anchors and encode positive anchors. Args: bbox_assigner (BaseAssigner): assign positive and negative boxes. a

A function warpper for building 3D detector or segmentor according to cfg. Should be deprecated in the future.

Limit the value into a period for periodic function. Args: val (torch.Tensor): The value to be converted. offset (float, optional

This is a post-processing function on the bboxes from Mono-3D task. If we want to perform projection visualization, we need to: 1. rotate

Sample specific number of ground truths. Args: num (int): Sampled number. Returns: list[dict]: Sampled groun

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

Show result by meshlab. Args: data (dict): Contain data from pipeline. result (dict): Predicted result from model. out_di

Convert results into format that is directly readable for meshlab. Args: points (np.ndarray): Points. gt_seg (np.ndarray): Ground

Generate targets of line primitive. Args: point_mask (torch.Tensor): Tensor to store the ground truth of mask.

Draw points on visualizer. Args: points (numpy.array | torch.tensor, shape=[N, 3+C]): points to visualize. vis (:obj:

Repeat x `num` times to form a list.

Given feats and indexes, returns the gathered feats. Args: feats (torch.Tensor): Features to be transposed and gathered

Write points into ``obj`` format for meshlab visualization. Args: points (np.ndarray): Points in shape (N, dim). out_filename (st

3d nms for aligned boxes. Args: boxes (torch.Tensor): Aligned box with shape [n, 6]. scores (torch.Tensor): Scores of each box.

Test with augmentation.

Convert 3D detection results to kitti format for evaluation and test submission. Args: net_outputs (list[np.ndarray]): Li

Multi-class nms for 3D boxes. Args: mlvl_bboxes (torch.Tensor): Multi-level boxes with shape (N, M). M is the dimensions of b

Convert points in camera coordinate to lidar coordinate. Args: points (np.ndarray, shape=[N, 3]): Points in camera coordinate. r_

Circular NMS. An object is only counted as positive if no other center with a higher confidence exists within a radius r using a bird-eye

Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP. Args: gt_annos (dict): Must from get_label_annos() in kitti_common

Directly extract features from the img backbone+neck.

Args: input: NC points

Forward function for training. Args: points (list[torch.Tensor]): List of points of shape [N, C]. img_metas (list): I

Args: win_inds indicates which windows a voxel belongs to. Voxels share a window have same inds. shape = [N,] Return:

Compute surface and line center of bounding boxes. Returns: torch.Tensor: Surface and line center of bounding boxes.

Compute semantic segmentation loss. Args: seg_logit (torch.Tensor): Predicted per-point segmentation logits \ of

Match points to corresponding line. Args: points (torch.Tensor): Points of input. corners (torch.Tensor): Eight corne

Multi-class nms in single batch. Args: obj_scores (torch.Tensor): Objectness score of bounding boxes. sem_scores (tor

Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth databas

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

Training data preparation. Args: index (int): Index for accessing the target data. Returns: dict: Training d

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

Add shared or separable branch.

Create a superset of inputs needed to run test or train batches. Args: input_shape (tuple): input batch dimensions n

Draw bbox on visualizer and change the color of points inside bbox3d. Args: bbox3d (numpy.array | torch.tensor, shape=[M, 7]):

Grab configs necessary to create a bbox_head. These are deep copied to allow for safe modification of parameters without influencing other te

Grab configs necessary to create a pts_bbox_head. These are deep copied to allow for safe modification of parameters without influencing othe

Grab configs necessary to create a vote_head. These are deep copied to allow for safe modification of parameters without influencing other te

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Convert the rotation difference to difference in sine function. Args: boxes1 (torch.Tensor): Original Boxes in shape (NxC), where

Generate anchors in a single range. Args: feature_size (list[float] | tuple[float]): Feature map size. It is eith

Convert continuous angle to a discrete class and a residual. Convert continuous angle to a discrete class and a small regression numb

Convert 2D detection results to kitti format for evaluation and test submission. Args: net_outputs (list[np.ndarray]): Li

Convert a list of bounding boxes to roi format. Args: bbox_list (list[torch.Tensor]): A list of bounding boxes corresponding

Calculate nearest 3D IoU. Note: This function first finds the nearest 2D boxes in bird eye view (BEV), and then calculates the 2D

Build segmentor.

Convert kitti locations, dimensions and angles to corners. format: center(xy), dims(xy), angles(clockwise when positive) Args: center

Inverse function to angle2class. Args: angle_cls (torch.Tensor): Angle class to decode. angle_res (torch.Tensor): Ang

Sigmoid function for input feature. Args: x (torch.Tensor): Input feature map with the shape of [B, N, H, W]. eps (float): Lower

Generate relative box corners based on length per dim and origin point. Args: dims (np.ndarray, shape=[N, ndim]): Array of length per dim

Given the raw data, generate the ground truth database. Args: dataset_class_name （str): Name of the input dataset. data_path (str

Encode points with backbone and decode into a semantic segmentation map of the same size as input. Args: points (torch.Te

Extract bounding boxes from 2d detector. Args: img (torch.Tensor): of shape (N, C, H, W) encoding input images. T

Extract features from points.

Extract features from points.

Extract features from points.

Extract features from points.

Extract features of points.

Flip the boxes in BEV along given BEV direction. In LIDAR coordinates, it flips the y (horizontal) or x (vertical) axis. Args: