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Point cloud learning is receiving increasing attention, however, mostexisting point cloud models lack the practical ability to deal with theunavoidable presence of unknown objects. This paper mainly discusses pointcloud learning under open-set settings, where we train the model without datafrom unknown classes and identify them in the inference stage. Basically, wepropose to solve open-set point cloud learning using a novel Point Cut-and-Mixmechanism consisting of Unknown-Point Simulator and Unknown-Point Estimatormodules. Specifically, we use the Unknown-Point Simulator to simulateout-of-distribution data in the training stage by manipulating the geometriccontext of partial known data. Based on this, the Unknown-Point Estimatormodule learns to exploit the point cloud's feature context for discriminatingthe known and unknown data. Extensive experiments show the plausibility ofopen-set point cloud learning and the effectiveness of our proposed solutions.Our code is available at \url{https://github.com/ShiQiu0419/pointcam}.

PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning