Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution

Published in AAAI, 2020

In this paper, we propose a new point-set learning framework named Pointwise Rotation-Invariant Network (PRIN), focusing on achieving rotation-invariance in point clouds. We construct spherical signals by Density-Aware Adaptive Sampling (DAAS) from sparse points and employ Spherical Voxel Convolution (SVC) to extract rotation-invariant features for each point. Our network can be applied to applications ranging from object classification, part segmentation, to 3D feature matching and label alignment.

Recommended citation: You, Y., Lou, Y., Liu, Q., Tai, Y. W., Ma, L., Lu, C., & Wang, W. (2020). Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution. In AAAI (pp. 12717-12724).