Publications
Detailed Information
Rotation-invariant local-to-global representation learning for 3D point cloud
Cited 0 time in
Web of Science
Cited 30 time in Scopus
- Authors
- Issue Date
- 2020-01
- Citation
- Advances in Neural Information Processing Systems, Vol.2020-December
- Abstract
- We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate for handling various geometric transformations, especially rotation, without explicit data augmentation with respect to the transformations. Our model takes advantage of multi-level abstraction based on graph convolutional neural networks, which constructs a descriptor hierarchy to encode rotation-invariant shape information of an input object in a bottom-up manner. The descriptors in each level are obtained from a neural network based on a graph via stochastic sampling of 3D points, which is effective in making the learned representations robust to the variations of input data. The proposed algorithm presents the state-of-the-art performance on the rotation-augmented 3D object recognition and segmentation benchmarks. We further analyze its characteristics through comprehensive ablative experiments.
- ISSN
- 1049-5258
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.