RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
This addresses the challenge of accurate 3D scene understanding for applications like robotics and augmented reality, representing a novel method rather than incremental improvement.
The paper tackles the problem of reconstructing continuous 3D surfaces with semantics from point clouds, presenting RangeUDF which surpasses state-of-the-art approaches on four datasets and demonstrates superior generalization across unseen datasets.
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.