Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

ICCV 2023

Jiaxiang Tang1, Hang Zhou2, Xiaokang Chen1, Tianshu Hu2, Errui Ding2, Jingdong Wang2 Gang Zeng1,

1 Peking University   2 Baidu  



Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.



  title={Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement},
  author={Tang, Jiaxiang and Zhou, Hang and Chen, Xiaokang and Hu, Tianshu and Ding, Errui and Wang, Jingdong and Zeng, Gang},
  journal={arXiv preprint arXiv:2303.02091},