Reconstructing objects with realistic materials from multi-view images is problematic, since it is highly ill-posed. Although the neural reconstruction approaches have exhibited impressive reconstruction ability, they are designed for objects with specific materials (e.g., diffuse or specular materials). To this end, we propose a novel framework for robust geometry and material reconstruction, where the geometry is expressed with the implicit signed distance field (SDF) encoded by a tensorial representation, namely TensoSDF. At the core of our method is the roughness-aware incorporation of the radiance and reflectance fields, which enables a robust reconstruction of objects with arbitrary reflective materials. Furthermore, the tensorial representation enhances geometry details in the reconstructed surface and reduces the training time. Finally, we estimate the materials using an explicit mesh for efficient intersection computation and an implicit SDF for accurate representation. Consequently, our method can achieve more robust geometry reconstruction, outperform the previous works in terms of relighting quality, and reduce 50% training times and 70% inference time.

翻译：从多视角图像中重建具有真实感材质的物体极具挑战性，因为该问题本质上是严重病态的。尽管神经重建方法已展现出令人印象深刻的重建能力，但它们通常针对特定材质（如漫反射材质或镜面反射材质）设计。为此，我们提出了一种新颖的鲁棒几何与材质重建框架，其中几何通过隐式有符号距离场（SDF）表达，并由张量化表示（即TensoSDF）编码。我们方法的核心是粗糙度感知的辐射场与反射场融合策略，这实现了对具有任意反射特性材质的鲁棒重建。此外，张量化表示增强了重建表面的几何细节，并缩短了训练时间。最后，我们采用显式网格进行高效的交点计算，并结合隐式SDF进行精确表示来估计材质。实验表明，我们的方法能够实现更鲁棒的几何重建，在重光照质量上超越先前工作，同时减少50%的训练时间和70%的推理时间。