TensoSDF：面向鲁棒几何与材质重建的粗糙度感知张量表示
TensoSDF: Roughness-aware Tensorial Representation for Robust Geometry and Material Reconstruction
AI总结：
本文提出TensoSDF框架，通过粗糙度感知融合辐射场与反射场并结合张量编码的隐式SDF，实现任意反射材质物体的鲁棒几何与材质重建，同时提升细节、减少训练与推理时间。
摘要：
从多视角图像重建具有真实材质的物体是一个具有挑战性的问题，因为它高度不适定。尽管神经重建方法已经展现出令人印象深刻的重建能力，但它们通常是为具有特定材质（例如漫反射或镜面反射材质）的物体设计的。为此，我们提出了一种用于鲁棒几何与材质重建的新框架，其中几何形状由通过张量表示编码的隐式有符号距离场（SDF）表达，即 TensoSDF。我们方法的核心是粗糙度感知地融合辐射场与反射场，这使得能够对具有任意反射材质的物体进行鲁棒重建。此外，张量表示增强了重建表面中的几何细节，并减少了训练时间。最后，我们使用显式网格进行高效的求交计算，并使用隐式 SDF 进行精确表示来估计材质。因此，我们的方法能够实现更鲁棒的几何重建，在重新光照质量方面优于先前工作，并将训练时间减少 50%，推理时间减少 70%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
