  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.
