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.

本研究提出了RangeUDF框架，使用基于隐式表示的方法从点云中恢复连续3D场景表面的几何和语义，通过表面导向的语义分割模块和范围感知的带符号距离函数，实现了连续表面的语义建模和重建，并在多个点云数据集上比较实验表明，该框架明显优于现有方法。

RangeUDF: 从3D点云中语义表面重建