基于刚性混合样本的点云正则化策略
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
摘要：
数据增强是一种有效的正则化策略，以减轻过拟合，这是一个固有的缺陷，深神经网络。然而，数据增强很少被认为是点云处理，尽管许多研究提出了各种增强方法的图像数据。实际上，正则化对于点云是必不可少的，因为由于数据集较小，点云更容易缺乏通用性。本文提出了一种刚性子集混合（RSMix），这是一种新颖的点云数据增强方法，通过用另一个样本的形状保留子集替换部分样本来生成虚拟混合样本。RSMix通过使用邻近函数从每个样本中提取子集而不变形，从而保留点云样本的结构信息。考虑到点云的无序结构和非网格化等特点，对邻域函数进行了精心设计。实验证明，RSMix成功地正则化了深度神经网络，并在形状分类方面取得了显着改善。我们还分析了数据增强的各种组合，包括基于大量消融研究的单视图和多视图评价的RSMix。
英文摘要：
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix), a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
