PhyIR: Physics-based Inverse Rendering for Panoramic Indoor Images

Zhen Li, Lingli Wang, Xiang Huang, Cihui Pan, Jiaqi Yang

科研成果: 书/报告/会议事项章节会议稿件同行评审

21 引用 (Scopus)

摘要

Inverse rendering of complex material such as glossy, metal and mirror material is a long-standing ill-posed problem in this area, which has not been well solved. Previous approaches cannot tackle them well due to simplified BRDF and unsuitable illumination representations. In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance. Our framework estimates geometry, material and Spatially-Coherent (SC) illumination from a single indoor panorama. Due to the lack of panoramic datasets with completed SVBRDF and full-spherical light probes, we introduce an artist-designed dataset named FutureHouse with high-quality geometry, SVBRDF and per-pixel Spatially-Varying (SV) lighting. To ensure the coherence of SV lighting, a novel SC loss is proposed. Extensive experiments on both synthetic and real-world data show that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively, and is able to produce photorealistic results for a number of applications such as dynamic virtual object insertion.

源语言英语
主期刊名Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版商IEEE Computer Society
12703-12713
页数11
ISBN(电子版)9781665469463
DOI
出版状态已出版 - 2022
活动2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, 美国
期限: 19 6月 202224 6月 2022

出版系列

姓名Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2022-June
ISSN(印刷版)1063-6919

会议

会议2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
国家/地区美国
New Orleans
时期19/06/2224/06/22

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