Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
This addresses the challenge of handling geometric transformations like rotation in 3D point cloud analysis for applications such as object recognition and segmentation, with incremental improvements in robustness.
The paper tackled the problem of learning rotation-invariant representations for 3D point clouds without explicit data augmentation, achieving state-of-the-art performance on rotation-augmented 3D object recognition and segmentation benchmarks.
We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate to handle 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, and we further analyze its characteristics through comprehensive ablative experiments.