  The continual improvement of 3D sensors has driven the development of
algorithms to perform point cloud analysis. In fact, techniques for point cloud
classification and segmentation have in recent years achieved incredible
performance driven in part by leveraging large synthetic datasets.
Unfortunately these same state-of-the-art approaches perform poorly when
applied to incomplete point clouds. This limitation of existing algorithms is
particularly concerning since point clouds generated by 3D sensors in the real
world are usually incomplete due to perspective view or occlusion by other
objects. This paper proposes a general model for partial point clouds analysis
wherein the latent feature encoding a complete point clouds is inferred by
applying a local point set voting strategy. In particular, each local point set
constructs a vote that corresponds to a distribution in the latent space, and
the optimal latent feature is the one with the highest probability. This
approach ensures that any subsequent point cloud analysis is robust to partial
observation while simultaneously guaranteeing that the proposed model is able
to output multiple possible results. This paper illustrates that this proposed
method achieves state-of-the-art performance on shape classification, part
segmentation and point cloud completion.
