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


  • Yang You Shanghai Jiao Tong University
  • Yujing Lou Shanghai Jiao Tong University
  • Qi Liu Shanghai Jiao Tong University
  • Yu-Wing Tai Tencent
  • Lizhuang Ma Shanghai Jiao Tong University
  • Cewu Lu Shanghai Jiao Tong University
  • Weiming Wang Shanghai Jiao Tong University




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. Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. We show that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide theoretical analysis for the rotation-invariance achieved by our methods.




How to Cite

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. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12717-12724. https://doi.org/10.1609/aaai.v34i07.6965



AAAI Technical Track: Vision