  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.
