@inproceedings{ye2023intrinsicnerf,
author = {Ye, Weicai and Chen, Shuo and Bao, Chong and Bao,  Hujun and Pollefeys, Marc and Cui, Zhaopeng and Zhang, Guofeng},
title = {IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis},
booktitle = {ICCV 2023},
year = {2023},
month = {September},
abstract = {Existing inverse rendering combined with neural rendering methods can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based neural rendering method and can extend its application to room-scale scenes. Since intrinsic decomposition is a fundamentally under-constrained inverse problem, we propose a novel distance-aware point sampling and adaptive reflectance iterative clustering optimization method, which enables IntrinsicNeRF with traditional intrinsic decomposition constraints to be trained in an unsupervised manner, resulting in multi-view consistent intrinsic decomposition results. To cope with the problem that different adjacent instances of similar reflectance in a scene are incorrectly clustered together, we further propose a hierarchical clustering method with coarse-to-fine optimization to obtain a fast hierarchical indexing representation. It supports compelling real-time augmented applications such as recoloring and illumination variation. Extensive experiments and editing samples on both object-specific/room-scale scenes and synthetic/real-word data demonstrate that we can obtain consistent intrinsic decomposition results and high-fidelity novel view synthesis even for challenging sequences.},
url = {http://approjects.co.za/?big=en-us/research/publication/intrinsicnerf-learning-intrinsic-neural-radiance-fields-for-editable-novel-view-synthesis/},
}