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 this https URL

本文提出了一种新的点云补全方法 ProxyFormer，通过代理信息进行现有和待预测的部分之间通信，使用特征和位置提取器将信息融合到点代理中，通过缺失部分敏感转换器获取更合理的位置信息，对缺失代理进行校准，使得预测的点代理更加适合后续的粗到细过程，实验结果表明，我们的方法在多个基准数据集上优于现有的补全网络，并具有最快的推理速度。