@inproceedings{
yang2024robir,
title={Rob{IR}: Robust Inverse Rendering for High-Illumination Scenes},
author={Ziyi Yang and Chenyanzhen and Xinyu Gao and YazhenYuan and Wu Yu and Xiaowei Zhou and Xiaogang Jin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=y7oxY5pq4j}
}
Implicit representation has opened up new possibilities for inverse rendering. However, existing implicit neural inverse rendering methods struggle to handle strongly illuminated scenes with significant shadows and slight reflections. The existence of shadows and reflections can lead to an inaccurate understanding of the scene, making precise factorization difficult. To this end, we present RobIR, an implicit inverse rendering approach that uses ACES tone mapping and regularized visibility estimation to reconstruct accurate BRDF of the object. By accurately modeling the indirect radiance field, normal, visibility, and direct light simultaneously, we are able to accurately decouple environment lighting and the object's PBR materials without imposing strict constraints on the scene. Even in high-illumination scenes with shadows and specular reflections, our method can recover high-quality albedo and roughness with no shadow interference. RobIR outperforms existing methods in both quantitative and qualitative evaluations.
RobIR: Robust Inverse Rendering for High-Illumination Scenes
Ziyi Yang Chenyanzhen Xinyu Gao YazhenYuan Wu Yu Xiaowei Zhou Xiaogang Jin