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

Understand

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown.

• In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis.
• We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space.
• In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point.