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:我们提出了一个场景级别的反渲染框架,利用多视角图像将场景分解为几何形状,SVBRDF和三维空间变化照明。由于多视角图像提供了关于场景的各种信息,因此在对象级别反渲染中对多视角图像已经司空见惯。然而,由于缺少多视角HDR合成数据集,因此场景级别反渲染主要使用单视角图像进行研究。我们通过扩展OpenRooms数据集和设计有效的流程来处理多视角图像并分割空间变化照明,成功地使用多视角图像执行场景级别反渲染。我们的实验表明,所提出的方法不仅比基于单视角的方法性能更好,而且对未见过的实际场景具有稳健的性能。此外,我们精心设计的三维空间变化照明体积允许在任何三维位置进行逼真的对象插入。