We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape, radiance, and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further, to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR, movies, games, etc. Project page: https://shinobi.aengelhardt.com Video: https://www.youtube.com/watch?v=iFENQ6AcYd8&feature=youtu.be
@article{arxiv.2401.10171,
title = {SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild},
author = {Andreas Engelhardt and Amit Raj and Mark Boss and Yunzhi Zhang and Abhishek Kar and Yuanzhen Li and Deqing Sun and Ricardo Martin Brualla and Jonathan T. Barron and Hendrik P. A. Lensch and Varun Jampani},
journal= {arXiv preprint arXiv:2401.10171},
year = {2024}
}
Comments
Accepted by IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024). Updated supplementary material and acknowledgements