Abstract
Robust point cloud classification is crucial for real-world applications, as consumer-type 3D sensors often yield partial and noisy data, degraded by various artifacts. In this work we propose a general ensemble framework, based on partial point cloud sampling. Each ensemble member is exposed to only partial input data. Three sampling strategies are used jointly, two local ones, based on patches and curves, and a global one of random sampling. We demonstrate the robustness of our method to various local and global degradations. We show that our framework significantly improves the robustness of top classification netowrks by a large margin. Our experimental setting uses the recently introduced ModelNet-C database by Ren et al.[24], where we reach SOTA both on unaugmented and on augmented data. Our unaugmented mean Corruption Error (mCE) is 0.64 (current SOTA is 0.86) and 0.50 for augmented data (current SOTA is 0.57). We analyze and explain these remarkable results through diversity analysis.
Abstract (translated)
点云分类的可靠性对于实际应用程序是至关重要的,因为消费类型的三维传感器通常产生不完整和噪声过多的数据,受到各种 artifacts 的退化。在本文中,我们提出了基于部分点云采样的一般群体框架。每个群体成员只暴露到部分输入数据。我们联合使用了三种采样策略,其中两种基于patches和曲线,一种是基于随机采样的全球策略。我们证明了我们方法的鲁棒性对各种局部和全局退化的适应性。我们表明我们的框架极大地提高了顶级分类算法的鲁棒性。我们的实验设置使用Ren等人最近引入的ModelNet-C数据库,在未增强和增强数据上都达到了当前最高水平(SOTA)。我们未增强的平均 corruption 错误(mCE)为0.64(当前SOTA为0.86),而增强数据上的mCE为0.50(当前SOTA为0.57)。我们通过多样性分析分析了这些令人瞩目的结果。
URL
https://arxiv.org/abs/2303.11419
