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[arXiv]
[bibtex]@InProceedings{Choi_2023_CVPR, author = {Choi, JunYong and Lee, SeokYeong and Park, Haesol and Jung, Seung-Won and Kim, Ig-Jae and Cho, Junghyun}, title = {MAIR: Multi-View Attention Inverse Rendering With 3D Spatially-Varying Lighting Estimation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {8392-8401} }
MAIR: Multi-View Attention Inverse Rendering With 3D Spatially-Varying Lighting Estimation
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.
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