RangeUDF: 点群からの意味的サーフェス再構成
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
任意のトポロジーに対応するレンジ考慮型の符号なし距離場と表面指向の意味分割を組み合わせ、点群から連続的な3D表面の形状と意味を同時に復元する手法を提案した。
著者: Bing Wang, Zhengdi Yu, Bo Yang, Jie Qin, Toby Breckon, Ling Shao, Niki Trigoni, Andrew Markham
分類: cs.CV, cs.AI, cs.GR, cs.LG, cs.RO
原文アブストラクト
We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields or signed distance fields which can only model closed 3D surfaces, our approach is not restricted to any type of topology. Being different from the existing unsigned distance fields, our framework does not suffer from any surface ambiguity. In addition, our RangeUDF can jointly estimate precise semantics for continuous surfaces. The key to our approach is a range-aware unsigned distance function together with a surface-oriented semantic segmentation module. Extensive experiments show that RangeUDF clearly surpasses state-of-the-art approaches for surface reconstruction on four point cloud datasets. Moreover, RangeUDF demonstrates superior generalization capability across multiple unseen datasets, which is nearly impossible for all existing approaches.
