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材质的物体极具挑战性，因为这是一个高度病态的问题。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，其中几何由张量表示编码的隐式符号距离场（SDF）表达，即TensoSDF。该方法的核心是粗糙度感知的辐射场与反射场融合机制，可实现对任意反射率材质物体的鲁棒重建。此外，张量表示能增强重建表面几何细节并减少训练时间。最后，我们结合显式网格（用于高效求交计算）与隐式SDF（用于精确表示）进行材质估计。实验表明，本方法可实现更鲁棒的几何重建，在重光照质量上超越此前工作，并减少50%训练时间与70%推理时间。