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.

翻译：我们提出了一种场景级逆渲染框架，通过使用多视角图像将场景分解为几何结构、SVBRDF以及三维空间变化光照。由于多视角图像能提供丰富的场景信息，其在物体级逆渲染中已被广泛采用。然而，受限于缺乏多视角高动态范围合成数据集，场景级逆渲染主要依赖于单视角图像研究。通过扩展OpenRooms数据集、设计高效的多视角图像处理流水线以及拆分空间变化光照，我们成功实现了基于多视角图像的场景级逆渲染。实验表明，所提方法不仅性能优于单视角方法，在未见过的真实场景中亦展现出鲁棒性。此外，精细的三维空间变化光照体可支持任意三维位置的光照合成物体插入。