Photometric Inverse Rendering: Shading Cues Modeling and Surface Reflectance Regularization
<|reference_start|>Photometric Inverse Rendering: Shading Cues Modeling and Surface Reflectance Regularization: This paper addresses the problem of inverse rendering from photometric images. Existing approaches for this problem suffer from the effects of self-shadows, inter-reflections, and lack of constraints on the surface reflectance, leading to inaccurate decomposition of reflectance and illumination due to the ill-posed nature of inverse rendering. In this work, we propose a new method for neural inverse rendering. Our method jointly optimizes the light source position to account for the self-shadows in images, and computes indirect illumination using a differentiable rendering layer and an importance sampling strategy. To enhance surface reflectance decomposition, we introduce a new regularization by distilling DINO features to foster accurate and consistent material decomposition. Extensive experiments on synthetic and real datasets demonstrate that our method outperforms the state-of-the-art methods in reflectance decomposition.<|reference_end|>
@article{bao2024photometric,
    title={Photometric Inverse Rendering: Shading Cues Modeling and Surface
  Reflectance Regularization},
    author={Jingzhi Bao, Guanying Chen, Shuguang Cui},
    journal={arXiv preprint arXiv:2408.06828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06828},
    primaryClass={cs.CV}
}