  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.
