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。