  Point cloud learning is receiving increasing attention, however, most
existing point cloud models lack the practical ability to deal with the
unavoidable presence of unknown objects. This paper mainly discusses point
cloud learning under open-set settings, where we train the model without data
from unknown classes and identify them in the inference stage. Basically, we
propose to solve open-set point cloud learning using a novel Point Cut-and-Mix
mechanism consisting of Unknown-Point Simulator and Unknown-Point Estimator
modules. Specifically, we use the Unknown-Point Simulator to simulate
out-of-distribution data in the training stage by manipulating the geometric
context of partial known data. Based on this, the Unknown-Point Estimator
module learns to exploit the point cloud's feature context for discriminating
the known and unknown data. Extensive experiments show the plausibility of
open-set point cloud learning and the effectiveness of our proposed solutions.
Our code is available at \url{https://github.com/ShiQiu0419/pointcam}.
