  Point clouds are the native output of many real-world 3D sensors. 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. In this paper, we propose a novel
learnable convolution layer for processing 3D point cloud data directly.
Instead of discretizing points into fixed voxels, we deform our learnable 3D
filters to match with the point cloud shape. We propose to combine voxelized
backbone networks with our deformable filter layer at 1) the network input
stream and 2) the output prediction layers to enhance point level reasoning. We
obtain state-of-the-art results on LiDAR semantic segmentation and producing a
significant gain in performance on LiDAR object detection.
