  We propose TensoIR, a novel inverse rendering approach based on tensor
factorization and neural fields. Unlike previous works that use purely
MLP-based neural fields, thus suffering from low capacity and high computation
costs, we extend TensoRF, a state-of-the-art approach for radiance field
modeling, to estimate scene geometry, surface reflectance, and environment
illumination from multi-view images captured under unknown lighting conditions.
Our approach jointly achieves radiance field reconstruction and
physically-based model estimation, leading to photo-realistic novel view
synthesis and relighting results. Benefiting from the efficiency and
extensibility of the TensoRF-based representation, our method can accurately
model secondary shading effects (like shadows and indirect lighting) and
generally support input images captured under single or multiple unknown
lighting conditions. The low-rank tensor representation allows us to not only
achieve fast and compact reconstruction but also better exploit shared
information under an arbitrary number of capturing lighting conditions. We
demonstrate the superiority of our method to baseline methods qualitatively and
quantitatively on various challenging synthetic and real-world scenes.

Computer Vision and Pattern Recognition