Compute a transform matrix by given quaternion and translation vector.

Empty annotation for single instance.

Convert objects of various python types to :obj:`torch.Tensor`. Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, :class:`S

Create info file in the form of data_infos={ 'metainfo': {'DATASET': 'SemanticKITTI'}, 'data_list': {

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

Method to pack the single input data. when the value in this dict is a list, it usually is in Augmentations Testing. Args:

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

Directly returns the positive and negative indices of samples. Args: assign_result (:obj:`AssignResult`): Mask assigning results

Perform normalization, padding and bgr2rgb conversion for img data based on ``BaseDataPreprocessor``, and voxelize point cloud if `voxel`

Generate the train/val infos from the raw data. Args: nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset. train_scenes

Get the pad_shape of each image based on data and pad_size_divisor.

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Returns

Private function to move the 3D bounding box annotation from `ann_info` field to the root of `results`. Args: results (di

Private function to load 2.5D bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Returns

Private function to load 3D mask annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Retur

Private function to load 3D panoptic segmentation annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset

Private function to load 3D semantic segmentation annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset

Add one sample of panoptic predictions and ground truths for evaluation. Args: semantic_preds (np.ndarray): Semantic pred

Add one batch of semantic predictions and ground truths. Args: semantic_preds (np.ndarray): Semantic predictions. gt_

Copying data to the target device and Performs normalization, padding and bgr2rgb conversion and stack based on ``BaseDataPreprocessor

Evaluate the predictions. Args: gt_labels (list[dict[np.ndarray]]): Ground Truth. seg_preds (list[dict[np.ndarray]]):

r"""Format the results to txt file. Refer to `ScanNet documentation <http://kaldir.vc.in.tum.de/scannet_benchmark/documentation>`_. A

Get panoptic results from mask predictions and corresponding class predictions. Args: class_preds (list[torch.Tensor]): C

Generate one 2D annotation record given various information on top of the 2D bounding box coordinates. Args: ann_rec (dict): Original

Get the 2D annotation records for a given `sample_data_token`. Args: sample_data_token (str): Sample data token belonging to a camera

Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation.

Get results of IOU metric. Returns: tuple(np.ndarray): iou of all class and each class.

Get IOU statistics of TP, FP and FN. Returns: tuple(np.ndarray): TP, FP, FN of all class.

Get results of PQ metric. Returns: tuple(np.ndarray): PQ, SQ, RQ of each class and all class.

Get voxel-wise segmentation label and point2voxel map. Args: res_coors (Tensor): The voxel coordinates of points, Nx3.

LaserMix transform function. Args: input_dict (dict): Result dict from loading pipeline. mix_results (dict): Mixed di

Encode features with sparse indices.

Panoptic Segmentation Evaluation. Evaluate the result of the panoptic segmentation. Args: gt_labels (list[dict[np.ndarray]]): Ground

PolarMix transform function. Args: input_dict (dict): Result dict from loading pipeline. mix_results (dict): Mixed di

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

Print results. Returns: dict[float]: The computed metrics. The keys are the names of the metrics, and the values are

Get voxel coordinates hash for np.unique(). Args: x (np.ndarray): The voxel coordinates of points, Nx3. Returns:

Reset class variables.

Prepare the info file for SemanticKITTI dataset. Args: info_prefix (str): The prefix of info filenames. out_dir (str): Output dir

Sparse Quantization for voxel coordinates used in Minkunet. Args: coords (np.ndarray): The voxel coordinates of points, Nx3.

Apply voxelization to point cloud. Args: points (List[Tensor]): Point cloud in one data batch. data_samples: (list[:o