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

• URL: http://arxiv.org/abs/2303.12368v2
• Date: Mon, 27 Mar 2023 04:32:11 GMT
• Title: MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation
• Authors: JunYong Choi and SeokYeong Lee and Haesol Park and Seung-Won Jung and Ig-Jae Kim and Junghyun Cho
• Abstract summary: 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. Our experiments show that the proposed method achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene.
• Score: 13.325800282424598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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