• 1Zhejiang University
  • 2University of California, San Diego
  • 3Kingstar Technology Inc.
  • 4Adobe Research

  • * Equal Contribution. † Equal advisory.


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 a 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 realworld scenes.

Results on TensoIR-Synthetic Dataset

* Physically-based rendering denotes rendering using the estimated BRDF and environment illumination
* All albedo results are scaled for each channel separately to aligned with ground-truth as is done by NeRFactor. The scaled factor is computed with the formula used in PhySG.



Relighting Under Unseen Lighting Conditions

Here we have a set of multi-view images captured under a single unknown lighting condition as input.
We compare with NeRFactor and InvRender on relighting under unseen lighting conditions.


Secondary-shading Modelling

(a) Visibility Modelling

We compare with NeRFactor on visibility modeling, showing the lighting visibility under individual directional lights (one light at a time, or OLAT).


Input View

Our Method


(b) Indirect Lighting Modelling

Rendered with only indirect light

Rendered with only direct light

Full Rendering



Some code for this website was borrowed from Nerfies and ClimateNeRF.