@inproceedings{
wang2024neurodin,
title={NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction},
author={Yifan Wang and Di Huang and Weicai Ye and Guofeng Zhang and Wanli Ouyang and Tong He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hVGAGU4TKk}
}
Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction. Although promising, SDF-based methods often fail to capture detailed geometric structures, resulting in visible defects. By comparing SDF-based volume rendering to density-based volume rendering, we identify two main factors within the SDF-based approach that degrade surface quality: SDF-to-density representation and geometric regularization. These factors introduce challenges that hinder the optimization of the SDF field. To address these issues, we introduce NeuRodin, a novel two-stage neural surface reconstruction framework that not only achieves high-fidelity surface reconstruction but also retains the flexible optimization characteristics of density-based methods. 
  NeuRodin incorporates innovative strategies that facilitate transformation of arbitrary topologies and reduce artifacts associated with density bias.
  Extensive evaluations on the Tanks and Temples and ScanNet++ datasets demonstrate the superiority of NeuRodin, showing strong reconstruction capabilities for both indoor and outdoor environments using solely posed RGB captures. Project website:
https://open3dvlab.github.io/NeuRodin/
NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
Yifan Wang Di Huang Weicai Ye Guofeng Zhang Wanli Ouyang Tong He