Evaluates the performance of the registration algorithm according to the evaluation protocol defined by the 3DMatch/Redwood datasets. The eva

Extract only those transformation matrices from the ground truth trajectory that are also in the estimated trajectory. Args: est_pai

xyz is a N x 3 matrix rgb is a N x 3 matrix and all color must range from [0, 1] or None normal is a N x 3 matrix and all normal range from [-1

r"""Extract features at the specified continuous coordinate matrix. Args: :attr:`query_coordinates` (:attr:`torch.FloatTensor`): a

r"""Create a sparse tensor coordinate map with the tensor stride. :attr:`field_map_key` (`CoordinateMapKey`): field map that a new sparse

* @brief Returns the registered message's handle * */

* @brief Returns the id of the category. * */

when center is True, the custom region_offset will be centered at the origin. Currently, for HYPER_CUBE, HYPER_CROSS with odd kernel sizes ca

* Coordinate management helper functions ****************************************************************************/

r"""create a new coordinate map and returns :attr:`coordinates`: `torch.FloatTensor` (`CUDA` if coordinate_map_type == `CoordinateMap

Perform Differentiable Optimal Transport in Log-space for stability

Perform Sinkhorn Normalization in Log-space for stability

r"""Compute the average of sparse tensor features Sum all sparse tensor features. All sparse tensors must have the same `coordinate_map_key`

r""" Convert color in range [0, 1] to [-0.5, 0.5]. If the color is in range [0, 255], use the argument `is_color_in_range_0_255=True`. `c

Generates a random 3D rotation matrix from axis and angle. Args: numpy_random_state: numpy random state object Returns:

Function that reads a trajectory saved in the 3DMatch/Redwood format to a numpy array. Format specification can be found at http://redwood-d

Function that reads a trajectory saved in the 3DMatch/Redwood format to a numpy array. Format specification can be found at http://redwood-d

Function that reads the trajectory information saved in the 3DMatch/Redwood format to a numpy array. Information file contains the variance-c

reserves space for the specified number of elements. Makes sure the old data fits. Exactly the same as resize(c). Use resize(0) to shrink to fit.