  Problems such as equipment defects or limited viewpoints will lead the
captured point clouds to be incomplete. Therefore, recovering the complete
point clouds from the partial ones plays an vital role in many practical tasks,
and one of the keys lies in the prediction of the missing part. In this paper,
we propose a novel point cloud completion approach namely ProxyFormer that
divides point clouds into existing (input) and missing (to be predicted) parts
and each part communicates information through its proxies. Specifically, we
fuse information into point proxy via feature and position extractor, and
generate features for missing point proxies from the features of existing point
proxies. Then, in order to better perceive the position of missing points, we
design a missing part sensitive transformer, which converts random normal
distribution into reasonable position information, and uses proxy alignment to
refine the missing proxies. It makes the predicted point proxies more sensitive
to the features and positions of the missing part, and thus make these proxies
more suitable for subsequent coarse-to-fine processes. Experimental results
show that our method outperforms state-of-the-art completion networks on
several benchmark datasets and has the fastest inference speed. Code is
available at https://github.com/I2-Multimedia-Lab/ProxyFormer.
