Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is less studied. In this paper, we present ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness, consisting of 15 common and realistic corruptions. Our evaluation shows a significant gap between the performances on ModelNet40 and ModelNet40-C for state-of-the-art (SOTA) models. To reduce the gap, we propose a simple but effective method by combining PointCutMix-R and TENT after evaluating a wide range of augmentation and test-time adaptation strategies. We identify a number of critical insights for future studies on corruption robustness in point cloud recognition. For instance, we unveil that Transformer-based architectures with proper training recipes achieve the strongest robustness. We hope our in-depth analysis will motivate the development of robust training strategies or architecture designs in the 3D point cloud domain. Our codebase and dataset are included in https://github.com/jiachens/ModelNet40-C
翻译:3D点云数据深心神经网络在现实世界中被广泛使用,特别是在安全关键应用中。然而,对3D点云数据的深度神经网络,特别是3D点云数据的深度神经网络被广泛使用,特别是在安全关键应用中。然而,它们反腐败的稳健性研究较少。在本文中,我们介绍了3D点云稳健性的第一个综合基准,即MedelNet40-C,由15个共同和现实的腐败构成。我们的评估显示,模型Net40和最先进模型模型40-C的绩效之间存在巨大差距。为了缩小差距,我们提议了一个简单而有效的方法,在评估一系列广泛的增强和测试时间适应战略后,将PointCutMix-R和TENT结合起来。我们在点云度识别中确定了关于腐败稳健性的未来研究的若干关键见解。例如,我们公布了具有适当培训配方的变形器结构能够取得最强的稳健性。我们希望我们的深入分析将激励在3D点云域发展稳健的培训战略或建筑设计。我们的代码库和数据集载于http://github.com/jiachens/ModelNet40-Cnet40-Cet40。