Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud

• URL: http://arxiv.org/abs/2010.03318v4
• Date: Wed, 31 Mar 2021 04:39:26 GMT
• Title: Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
• Authors: Seohyun Kim, Jaeyoo Park, Bohyung Han
• Abstract summary: We propose a local-to-global representation learning algorithm for 3D point cloud data. Our model takes advantage of multi-level abstraction based on graph convolutional neural networks. The proposed algorithm presents the state-of-the-art performance on the rotation-augmented 3D object recognition and segmentation benchmarks.
• Score: 42.86112554931754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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