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We propose a scene-level inverse rendering framework that uses multi-viewimages to decompose the scene into geometry, a SVBRDF, and 3D spatially-varyinglighting. Because multi-view images provide a variety of information about thescene, multi-view images in object-level inverse rendering have been taken forgranted. 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 usingmulti-view images by expanding OpenRooms dataset and designing efficientpipelines to handle multi-view images, and splitting spatially-varyinglighting. Our experiments show that the proposed method not only achievesbetter performance than single-view-based methods, but also achieves robustperformance on unseen real-world scene. Also, our sophisticated 3Dspatially-varying lighting volume allows for photorealistic object insertion inany 3D location.

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