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/.

翻译：摘要：近年来对点云分析的研究兴趣推动了面向三维模型的深度学习方法快速发展。然而，现有最先进模型对旋转不具备鲁棒性，这在实际应用中仍是未知先验并损害模型性能。本文提出一种新颖的逐块旋转不变网络（PaRot），通过特征解耦实现旋转不变性，并对任意旋转的样本生成一致的预测结果。具体而言，我们设计了一个孪生训练模块，借助旋转对从不同尺度定义的块（例如局部几何与全局形状）中解耦旋转不变特征与旋转等变特征。然而，解耦后的不变特征丢失了每个块的内禀姿态信息。为解决此问题，我们提出一种旋转不变几何关系，利用等变信息恢复不同尺度块的相对姿态。借助姿态信息，我们设计了一个层次化模块，实现面向三维形状学习的尺度内与尺度间特征聚合。此外，我们引入了嵌入旋转不变相对姿态信息的姿态感知特征传播过程。实验表明，我们的解耦模块能够提取高质量的旋转鲁棒特征，所提出的轻量级模型在旋转三维物体分类与部件分割任务中取得了具有竞争力的结果。项目页面已发布至：https://patchrot.github.io/。