Abstract
Point cloud is often regarded as a discrete sampling of Riemannian manifold and plays a pivotal role in the 3D image interpretation. Particularly, rotation perturbation, an unexpected small change in rotation caused by various factors (like equipment offset, system instability, measurement errors and so on), can easily lead to the inferior results in point cloud learning tasks. However, classical point cloud learning methods are sensitive to rotation perturbation, and the existing networks with rotation robustness also have much room for improvements in terms of performance and noise tolerance. Given these, this paper remodels the point cloud from the perspective of manifold as well as designs a manifold distillation method to achieve the robustness of rotation perturbation without any coordinate transformation. In brief, during the training phase, we introduce a teacher network to learn the rotation robustness information and transfer this information to the student network through online distillation. In the inference phase, the student network directly utilizes the original 3D coordinate information to achieve the robustness of rotation perturbation. Experiments carried out on four different datasets verify the effectiveness of our method. Averagely, on the Modelnet40 and ScanobjectNN classification datasets with random rotation perturbations, our classification accuracy has respectively improved by 4.92% and 4.41%, compared to popular rotation-robust networks; on the ShapeNet and S3DIS segmentation datasets, compared to the rotation-robust networks, the improvements of mIoU are 7.36% and 4.82%, respectively. Besides, from the experimental results, the proposed algorithm also shows excellent performance in resisting noise and outliers.
Abstract (translated)
点云通常被视为黎曼流形的离散采样，在三维图像解释中起着关键作用。特别是，旋转扰动（由各种因素如设备偏移、系统不稳定和测量误差等引起的意外小角度变化）很容易导致点云学习任务的结果变差。然而，传统的点云学习方法对旋转扰动非常敏感，并且现有的具有旋转鲁棒性的网络在性能和噪声容忍度方面仍有改进空间。鉴于此，本文从流形的角度重新构建了点云，并设计了一种流形蒸馏方法，在无需任何坐标变换的情况下实现对旋转扰动的鲁棒性。简而言之，在训练阶段，我们引入了一个教师网络来学习旋转鲁棒信息并通过在线蒸馏将其传递给学生网络。在推理阶段，学生网络直接利用原始的三维坐标信息来达到对旋转扰动的鲁棒性。实验结果表明，在四个不同的数据集上验证了该方法的有效性。平均而言，在含有随机旋转扰动的Modelnet40和ScanobjectNN分类数据集中，与流行的具有旋转鲁棒性的网络相比，我们的分类准确率分别提高了4.92%和4.41%；在ShapeNet和S3DIS分割数据集上，与这些具有旋转鲁棒性的网络相比，mIoU的提高分别为7.36%和4.82%。此外，从实验结果来看，所提出的算法还表现出对抗噪声和异常值的良好性能。
URL
https://arxiv.org/abs/2411.01748
