MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

Authors
Choi, JunYongLee, SeokYeongPark, HaesolJung, Seung-WonKim, Ig-JaeCho, Junghyun
Issue Date
2023-06
Publisher
IEEE COMPUTER SOC
Citation
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.8392 - 8401
Abstract
We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.
ISSN
1063-6919
URI
https://pubs.kist.re.kr/handle/201004/76425
DOI
10.1109/CVPR52729.2023.00811
Appears in Collections:
KIST Conference Paper > 2023
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