点群補完ネットワーク（PCN）
PCN: Point Completion Network
部分的な点群から物体の完全な形状を推定する学習ベース手法PCNを提案。対称性や意味クラスの仮定なしに生の点群を直接扱い、少ないパラメータで高精細な補完を実現した。
著者: Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert
分類: cs.CV, cs.RO
原文アブストラクト
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.
