MPVNN: Multi-resolution Point-Voxel Non-parametric Network for 3D Point Cloud Processing

Recent non-parametric networks have demonstrated the feasibility of point cloud tasks through skillfully combining Farthest point sampling, k-nearest neighbor, and pooling algorithms. However, these point-based algorithms struggle to capture features containing fine-grained spatial structures within a point cloud, thereby limiting the performance of non-parametric networks. To address it, we propose the Multi-resolution Point-Voxel Non-parametric Network (MPVNN) to enhance the structural information of voxelized point clouds at varying resolutions. Specifically, we design a Multi-Resolution Voxel Encoder (MR-VEnc) that processes point clouds with multiple resolutions and adopts trilinear interpolation to synthesize features with respect to 8 voxel grid vertices. Then, we amalgamate features derived from the point-based algorithms with those from MRVEnc to establish a point memory bank for subsequent tasks. Extensive experiments on ModelNet40, 3D-FUTURE, and ShapeNetPart demonstrate the superior performance of MPVNN in both classification and segmentation tasks.