Corpus ID: 207869702

Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology

@article{Zhao2019RotationIP,
  title={Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology},
  author={Chenchen Zhao and Jiaqi Yang and Xin Xiong and Angfan Zhu and Zhiguo Cao and Xin Li},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.00195}
}
Point cloud analysis is a fundamental task in 3D computer vision. Most previous works have conducted experiments on synthetic datasets with well-aligned data; while real-world point clouds are often not pre-aligned. How to achieve rotation invariance remains an open problem in point cloud analysis. To meet this challenge, we propose a novel approach toward achieving rotation-invariant (RI) representations by combining local geometry with global topology. In our local-global-representation (LGR… Expand
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