Multi-view Surface Reconstruction Using Normal and Reflectance Cues

• URL: http://arxiv.org/abs/2506.04115v1
• Date: Wed, 04 Jun 2025 16:09:16 GMT
• Title: Multi-view Surface Reconstruction Using Normal and Reflectance Cues
• Authors: Robin Bruneau, Baptiste Brument, Yvain Quéau, Jean Mélou, François Bernard Lauze, Jean-Denis Durou, Lilian Calvet,
• Abstract summary: We introduce a versatile framework that incorporates multi-view normal and optionally reflectance maps into radiance-based surface reconstruction.<n>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.<n>Our method excels in reconstructing fine-grained details and handling challenging visibility conditions.
• Score: 3.3190807913214293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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