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Papers/PhySG: Inverse Rendering with Spherical Gaussians for Phys...

PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting

Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely

2021-04-01CVPR 2021 1Surface Normals EstimationSurface ReconstructionDepth PredictionInverse RenderingImage Relighting
PaperPDF

Abstract

We present PhySG, 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 RGB input images. Our 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 reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.

Results

TaskDatasetMetricValueModel
Image EnhancementStanford-ORBHDR-PSNR21.81PhySG
Image EnhancementStanford-ORBLPIPS0.055PhySG
Image EnhancementStanford-ORBSSIM0.96PhySG
Surface Normals EstimationStanford-ORBCosine Distance0.17PhySG
Inverse RenderingStanford-ORBHDR-PSNR21.81PhySG

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