PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration

• URL: http://arxiv.org/abs/2302.02535v1
• Date: Mon, 6 Feb 2023 02:13:51 GMT
• Title: PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
• Authors: Dingxin Zhang, Jianhui Yu, Chaoyi Zhang, Weidong Cai
• Abstract summary: State-of-the-art models are not robust to rotations, which remains an unknown prior to real applications. We introduce a novel Patch-wise Rotation-invariant network (PaRot) Our disentanglement module extracts high-quality rotation-robust features and the proposed lightweight model achieves competitive results.
• Score: 16.75367717130046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent interest in point cloud analysis has led rapid progress in designing deep learning methods for 3D models. However, state-of-the-art models are not robust to rotations, which remains an unknown prior to real applications and harms the model performance. In this work, we introduce a novel Patch-wise Rotation-invariant network (PaRot), which achieves rotation invariance via feature disentanglement and produces consistent predictions for samples with arbitrary rotations. Specifically, we design a siamese training module which disentangles rotation invariance and equivariance from patches defined over different scales, e.g., the local geometry and global shape, via a pair of rotations. However, our disentangled invariant feature loses the intrinsic pose information of each patch. To solve this problem, we propose a rotation-invariant geometric relation to restore the relative pose with equivariant information for patches defined over different scales. Utilising the pose information, we propose a hierarchical module which implements intra-scale and inter-scale feature aggregation for 3D shape learning. Moreover, we introduce a pose-aware feature propagation process with the rotation-invariant relative pose information embedded. Experiments show that our disentanglement module extracts high-quality rotation-robust features and the proposed lightweight model achieves competitive results in rotated 3D object classification and part segmentation tasks. Our project page is released at: https://patchrot.github.io/.

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