News

• 04/12/2022 repository created
• 31/12/2022 code release for material-geometry network
• 17/01/2023 dataset release: InteriorVerse material-geometry part
• 24/02/2023 code release for lighting network
• 28/02/2023 pretrained model and testing data (object insertion) released

TODO

• Code release for Material-Geometry network
• Code release for Lighting network
• Release of pretrained model
• Dataset release: InteriorVerse material-geometry part
• Dataset release: InteriorVerse lighting part

This repository implements the paper "Learning-Based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing" in SIGGRAPH Asia'22. It includes training and testing code of material-geometry network (MGNet) and testing code of lighting network (LightNet).

Also check our following work: I²-SDF !

Pretrained models are available here, including MGNet and LightNet.

If you find our work is useful, please consider cite:

@inproceedings{zhu2022learning,
    author = {Zhu, Jingsen and Luan, Fujun and Huo, Yuchi and Lin, Zihao and Zhong, Zhihua and Xi, Dianbing and Wang, Rui and Bao, Hujun and Zheng, Jiaxiang and Tang, Rui},
    title = {Learning-Based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing},
    year = {2022},
    publisher = {ACM},
    url = {https://doi.org/10.1145/3550469.3555407},
    booktitle = {SIGGRAPH Asia 2022 Conference Papers},
    articleno = {6},
    numpages = {8}
}