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.

翻译：在保持精细细节的同时实现高保真度的三维表面重建仍然具有挑战性，尤其是在面对具有复杂反射属性的材料且缺乏密集视角设置的情况下。本文提出了一种通用框架，将多视角法线图及可选的反射率图整合到基于辐射度的表面重建中。我们的方法采用像素级的反射率与表面法线联合重参数化，将其表示为在模拟变化光照下的一组辐射度向量。该表述能够无缝集成到标准的表面重建流程中，例如传统的多视角立体视觉（MVS）框架或现代的神经体渲染（NVR）框架。当与后者结合时，我们的方法在多视角光度立体视觉（MVPS）基准数据集（包括DiLiGenT-MV、LUCES-MV和Skoltech3D）上达到了最先进的性能。特别地，我们的方法在重建细粒度细节和处理具有挑战性的可见性条件方面表现优异。本文是Brument等人先前会议论文（发表于IEEE/CVF计算机视觉与模式识别会议（CVPR）2024论文集）的扩展版本，其特点在于采用了加速且更鲁棒的算法，并进行了更广泛的实证评估。本文相关的代码与数据可在 https://github.com/RobinBruneau/RNb-NeuS2 获取。