NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction

• URL: http://arxiv.org/abs/2408.10178v1
• Date: Mon, 19 Aug 2024 17:36:35 GMT
• Title: NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
• Authors: Yifan Wang, Di Huang, Weicai Ye, Guofeng Zhang, Wanli Ouyang, Tong He,
• Abstract summary: Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction. We introduce NeuRodin, a novel two-stage neural surface reconstruction framework. NeuRodin achieves high-fidelity surface reconstruction and retains the flexible optimization characteristics of density-based methods.
• Score: 63.85586195085141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

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