  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
