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

Build loss function.

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

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

Concatenate a list of Boxes into a single Boxes. Args: boxes_list (list[:obj:`BaseInstance3DBoxes`]): List of boxes.

Decode regression into locations, dimensions, orientations. Args: reg (Tensor): Batch regression for each predict center2d poi

Transform network output for a batch into bbox predictions. Args: cls_scores (list[Tensor]): Box scores for each scale level

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

Rotate points by angles according to axis. Args: points (np.ndarray | torch.Tensor | list | tuple ): Points of shape (N,

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

Build head.

Limit the value into a period for periodic function. Args: val (torch.Tensor | np.ndarray): The value to be converted. offset

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

Compute loss of the head. Args: cls_scores (list[Tensor]): Box scores for each scale level, each is a 4D-tens

Convert input array to target data type. Args: input_array (tuple | list | np.ndarray | torch.Tensor | int |

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

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

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

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

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

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

Encode CameraInstance3DBoxes by locations, dimensions, orientations. Args: locations (Tensor): Center location for 3D boxes.

Extract features from points.

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

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

Build backbone.

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

Initialize conv layers as a prediction branch.

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

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

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

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]): Input pipeline. If None is given,

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

Find all boxes in which each point is. Args: points (torch.Tensor): Points in shape (1, M, 3) or (M, 3), 3 di

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

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

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

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

Extract features of multiple samples.

Flip the points along given BEV direction. Args: bev_direction (str): Flip direction (horizontal or vertical).

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

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

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

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

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

Computes one-to-one matching based on the weighted costs. This method assign each query prediction to a ground truth or background.

Build neck.

Build normalization layer specially designed for DLANet. Args: cfg (dict): The norm layer config, which should contain:

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

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

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

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

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

Shuffle the points. Returns: torch.Tensor: The shuffled index.

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

Multi-class NMS for 3D boxes. The IoU used for NMS is defined as the 2D IoU between BEV boxes. Args: mlvl_bboxes (torch.Tensor):

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

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

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

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

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

Args: input_ch(int) : the number of input channel out_features (list[str]): name of the layers whose outputs shoul

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 (

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

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

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

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

Build detector.

Directly extract features from the img backbone+neck.

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

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

Get the class of points according to coordinate type. Args: points_type (str): The type of points coordinate. The valid v

Generate targets of proposal module. Args: points (list[torch.Tensor]): Points of each batch. gt_bboxes_3d (list[

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

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

Project points in image coordinates to camera coordinates. Args: points (torch.Tensor): 2.5D points in 2D images, [N, 3],

Find the box in which each point is. Args: points (torch.Tensor): Points in shape (1, M, 3) or (M, 3), 3 dime

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

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