Concatenate a list of Points into a single Points. Args: points_list (list[:obj:`BasePoints`]): List of points. Returns:

Convert current points to a specific device. Args: device (str | :obj:`torch.device`): The name of the device. Returns:

Clone the Points. Returns: :obj:`BasePoints`: Box object with the same properties \ as self.

Build loss function.

str: The device of the points are on.

Args: feat: shape=[N, C], N is the voxel num in the batch. voxel_drop_lvl: shape=[N, ]. Indicates drop_level of the window the vo

Concatenate a list of Boxes into a single Boxes. Args: boxes_list (list[:obj:`BaseInstances3DBoxes`]): List of boxes. Re

Generate bboxes from vote head predictions. Args: points (torch.Tensor): Input points. bbox_preds (dict): Predictions

Compute loss. Args: bbox_preds (dict): Predictions from forward of vote head. points (list[torch.Tensor]): Input poin

Rotate points with the given rotation matrix or angle. Args: rotation (float, np.ndarray, torch.Tensor): Rotation matrix

Convert action.

Build head.

Translate points with the given translation vector. Args: trans_vector (np.ndarray, torch.Tensor): Translation ve

Decode predicted parts to bbox3d. Args: bbox_out (dict): Predictions from model, should contain keys below. - ce

Make sparse convolution module. Args: in_channels (int): the number of input channels out_channels (int): the number of out chann

Convert points from `src` mode to `dst` mode. Args: point (tuple | list | np.ndarray | torch.Tensor | BasePoints)

Convert self to ``dst`` mode. Args: dst (:obj:`CoordMode`): The target Box mode. rt_mat (np.ndarray | torch.Tensor):

Flip the points in BEV along given BEV direction.

Convert detection results to a list of numpy arrays. Args: bboxes (torch.Tensor): Bounding boxes with shape of (n, 5). labels (to

Clone the Boxes. Returns: :obj:`BaseInstance3DBoxes`: Box object with the same properties \ as self.

Find the box which the points are in. Args: points (torch.Tensor): Points in shape (N, 3). Returns: torch.Te

Convert boxes from `src` mode to `dst` mode. Args: box (tuple | list | np.ndarray | torch.Tensor | BaseInstance3D

Forward. Args: k (int): number of nearest neighbors. xyz (Tensor): (B, N, 3) if transposed == False, else (B, 3, N).

Build backbone.

Training forward function. Args: points (list[torch.Tensor]): Point cloud of each sample. img_metas (list[dict]): Met

Create a new box object with data. The new box and its tensor has the similar properties \ as self and self.tensor, respectively.

torch.Shape: Shape of points.

Build detector.

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

Convert a rotated boxes in XYWHR format to XYXYR format. Args: boxes_xywhr (torch.Tensor): Rotated boxes in XYWHR format. Returns:

Build PointNet2 set abstraction (SA) module. Args: cfg (None or dict): The SA module config, which should contain: - type (st

Extract features from points.

Extract features of multiple samples.

KITTI evaluation. Args: gt_annos (list[dict]): Contain gt information of each sample. dt_annos (list[dict]): Contain detected inf

Test function without augmentaiton.

Get annotation info according to the given index. Args: index (int): Index of the annotation data to get. Returns:

Convert multi-modality detection results into 2D results. Project the predicted 3D bbox to 2D image plane and visualize them. Args:

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

Apply transformation to input point cloud. Args: pcd (torch.Tensor): The point cloud to be transformed. coords_type (str): 'DEPTH

Build neck.

Get the type and mode of box structure. Args: box_type (str): The type of box structure. The valid value are "LiDAR", "Camera

Merge augmented detection 3D bboxes and scores. Args: aug_results (list[dict]): The dict of detection results. The dict conta

Project points in camera coordinates to image coordinates. Args: points_3d (np.ndarray): Points in shape (N, 3) proj_mat (np.ndar

Scale the points with horizontal and vertical scaling factors. Args: scale_factors (float): Scale factors to scale the points.

Convert current boxes to a specific device. Args: device (str | :obj:`torch.device`): The name of the device. Returns:

Load data using input pipeline and extract data according to key. Args: index (int): Index for accessing the target data.

Get data loading pipeline in self.show/evaluate function. Args: pipeline (list[dict] | None): Input pipeline. If None is given, \

Rotate points by angles according to axis. Args: points (torch.Tensor): Points of shape (N, M, 3). angles (torch.Tensor): Vector

input: Nx3, write points to filename as PLY format.

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

Encode ground truth to prediction targets. Args: gt_bboxes_3d (BaseInstance3DBoxes): Ground truth bboxes \ with s

Limit the yaw to a given period and offset. Args: offset (float): The offset of the yaw. period (float): The expected

Create a new point object with data. The new point and its tensor has the similar properties \ as self and self.tensor, respectiv

Calculate 3D overlaps of two boxes. Note: This function calculates the overlaps between ``boxes1`` and ``boxes2``, ``

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

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

Parse the package dependencies listed in a requirements file but strips specific versioning information. Args: fname (str): path to r

Initialization before data preparation. Args: results (dict): Dict before data preprocessing. - img_fields (list

Generate targets for primitive z and primitive xy. Args: point_mask (torch.Tensor): Tensor to store the ground tr

Create reduced point clouds for given info. Args: data_path (str): Path of original data. info_path (str): Path of data info.

Load a configuration as a python module.

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

Calculate Chamfer Distance of two sets. Args: src (torch.Tensor): Source set with shape [B, N, C] to calculate Chamfer Distan

Evaluation in Lyft protocol. Args: results (list[dict]): Testing results of the dataset. metric (str | list[str]): Me

Extract and return the data corresponding to key in result dict. ``results`` is a dict output from `pipeline(input_dict)`, which is the l

Generate grid anchors in multiple feature levels. Args: featmap_sizes (list[tuple]): List of feature map sizes in

Initialize a model from config file, which could be a 3D detector or a 3D segmentor. Args: config (str or :obj:`mmcv.Config`): Config

Compute loss. Args: bbox_preds (dict): Predictions from forward of h3d bbox head. points (list[torch.Tensor]): Input

Match points to plane. Args: plane (torch.Tensor): Equation of the plane. points (torch.Tensor): Points of input.