Shape completion, the problem of estimating the complete geometry of objects
from partial observations, lies at the core of many vision and robotics
applications. In this work, we propose Point Completion Network (PCN), a novel
learning-based approach for shape completion. Unlike existing shape completion
methods, PCN directly operates on raw point clouds without any structural
assumption (e.g. symmetry) or annotation (e.g. semantic class) about the
underlying shape. It features a decoder design that enables the generation of
fine-grained completions while maintaining a small number of parameters. Our
experiments show that PCN produces dense, complete point clouds with realistic
structures in the missing regions on inputs with various levels of
incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.

本文提出了一种新的基于学习的方法 ——Point Completion Network（PCN），该方法直接在原始点云上操作而不需要任何结构假设或注释，并且其解码器设计可以在保持参数数量较小的同时生成精细的完整点云。实验结果表明，PCN 在处理各种不完整和噪声的输入时可以产生密集、完整、具有现实结构的点云。