SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement

• URL: http://arxiv.org/abs/2211.08250v1
• Date: Tue, 15 Nov 2022 15:59:09 GMT
• Title: SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement
• Authors: Zhaofan Qiu and Yehao Li and Yu Wang and Yingwei Pan and Ting Yao and Tao Mei
• Abstract summary: We propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net. The embedded Selective Position variant' procedure relies on an attention mechanism that can effectively attend to the underlying rotation condition of the input. We demonstrate the merits of the SPE-Net and the associated hypothesis on four benchmarks, showing evident improvements on both rotated and unrotated test data over SOTA methods.
• Score: 118.20816888815658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net. The embedded ``Selective Position Encoding (SPE)'' procedure relies on an attention mechanism that can effectively attend to the underlying rotation condition of the input. Such encoded rotation condition then determines which part of the network parameters to be focused on, and is shown to efficiently help reduce the degree of freedom of the optimization during training. This mechanism henceforth can better leverage the rotation augmentations through reduced training difficulties, making SPE-Net robust against rotated data both during training and testing. The new findings in our paper also urge us to rethink the relationship between the extracted rotation information and the actual test accuracy. Intriguingly, we reveal evidences that by locally encoding the rotation information through SPE-Net, the rotation-invariant features are still of critical importance in benefiting the test samples without any actual global rotation. We empirically demonstrate the merits of the SPE-Net and the associated hypothesis on four benchmarks, showing evident improvements on both rotated and unrotated test data over SOTA methods. Source code is available at https://github.com/ZhaofanQiu/SPE-Net.

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