PhySG: Inverse Rendering With Spherical Gaussians for Physics-Based Material Editing and Relighting

Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 5453-5462

We present an end-to-end inverse rendering pipeline that includes a fully differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images. Our rendering framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstruction not only can render novel viewpoints, but also enables physics-based appearance editing of materials and illumination.