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，该框架利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。由于多视角图像提供了场景的多样化信息，在物体级逆渲染中采用多视角图像已成为普遍做法。然而，由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单视角图像进行研究。通过扩展OpenRooms数据集并设计高效的多视角图像处理管线与空间变化光照分离方法，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，所提出方法不仅优于基于单视角的方法，而且在未见过的真实场景中同样展现了鲁棒性能。此外，我们精细的三维空间变化光照场能够支持在三维空间中任意位置进行照片级真实感的物体插入。