Multi-view Surface Reconstruction Using Normal and Reflectance Cues
Abstract
Achieving high-fidelity 3D surface reconstruction while preserving fine details remains challenging, especially in the presence of materials with complex reflectance properties and without a dense-view setup. In this paper, we introduce a versatile framework that incorporates multi-view normal and optionally reflectance maps into radiance-based surface reconstruction. Our approach employs a pixel-wise joint re-parametrization of reflectance and surface normals, representing them as a vector of radiances under simulated, varying illumination. This formulation enables seamless incorporation into standard surface reconstruction pipelines, such as traditional multi-view stereo (MVS) frameworks or modern neural volume rendering (NVR) ones. Combined with the latter, our approach achieves state-of-the-art performance on multi-view photometric stereo (MVPS) benchmark datasets, including DiLiGenT-MV, LUCES-MV and Skoltech3D. In particular, our method excels in reconstructing fine-grained details and handling challenging visibility conditions. The present paper is an extended version of the earlier conference paper by Brument et al. (in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024), featuring an accelerated and more robust algorithm as well as a broader empirical evaluation. The code and data relative to this article is available at https://github.com/RobinBruneau/RNb-NeuS2.
Cite
@article{arxiv.2506.04115,
title = {Multi-view Surface Reconstruction Using Normal and Reflectance Cues},
author = {Robin Bruneau and Baptiste Brument and Yvain Quéau and Jean Mélou and François Bernard Lauze and Jean-Denis Durou and Lilian Calvet},
journal= {arXiv preprint arXiv:2506.04115},
year = {2026}
}
Comments
22 pages, 15 figures, 11 tables. Accepted to IJCV. A thorough qualitative and quantitive study is available in the supplementary material at https://drive.google.com/file/d/1KDfCKediXNP5Os954TL_QldaUWS0nKcD/view?usp=drive_link. The project page can be accessed via https://robinbruneau.github.io/publications/rnb_neus2.html. The source code is available at https://github.com/RobinBruneau/RNb-NeuS2