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Applying colored point cloud registration RegistrationResult with fitness=8.763667e-01, inlier_rmse=1.457778e-02, and correspondence_set size of 2084 Access transformation to get result. To borrow the success of 2D convolutional network architectures, a majority of popular 3D perception models voxelize the points, which can result in a loss of local geometric details that cannot be recovered. Point clouds are the native output of many real-world 3D sensors. Deformable Filter Convolution for Point Cloud Reasoning.

Distinguished by a pipeline syntax for point cloud read, filter, and write tasks. Provides format translation, geospatial transformations, and point cloud filtering.
