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. Our code is available at: https://github.com/yossilevii100/EPiC

翻译：鲁棒的点云分类对于实际应用至关重要，因为消费级3D传感器常因各种伪影退化而产生局部且含噪的数据。本文提出了一种基于局部点云采样的通用集成框架。每个集成成员仅处理部分输入数据。我们联合使用了三种采样策略：两种基于局部（基于面片和曲线）的采样策略，以及一种全局随机采样策略。我们验证了该方法对多种局部与全局退化的鲁棒性，并证明该框架能显著提升顶尖分类网络的鲁棒性，性能提升幅度较大。实验采用Ren等人[24]最新引入的ModelNet-C数据库，在未增强数据和增强数据上均达到当前最优水平。未增强数据的平均损坏误差（mCE）为0.64（当前最优为0.86），增强数据为0.50（当前最优为0.57）。我们通过多样性分析对这些显著结果进行了解释。代码开源地址：https://github.com/yossilevii100/EPiC