MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation
This work addresses scene-level inverse rendering for computer vision and graphics applications, but it is incremental as it builds on existing datasets and methods.
The authors tackled scene-level inverse rendering by using multi-view images to decompose scenes into geometry, SVBRDF, and 3D spatially-varying lighting, achieving better performance than single-view methods and robust results on unseen real-world scenes.
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.