DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

• URL: http://arxiv.org/abs/2111.00140v1
• Date: Sat, 30 Oct 2021 01:59:39 GMT
• Title: DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
• Authors: Wenzheng Chen and Joey Litalien and Jun Gao and Zian Wang and Clement Fuji Tsang and Sameh Khamis and Or Litany and Sanja Fidler
• Abstract summary: We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables. In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing. Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
• Score: 78.91753256634453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers. Many previous learning-based approaches for inverse graphics adopt rasterization-based renderers and assume naive lighting and material models, which often fail to account for non-Lambertian, specular reflections commonly observed in the wild. In this work, we propose DIBR++, a hybrid differentiable renderer which supports these photorealistic effects by combining rasterization and ray-tracing, taking the advantage of their respective strengths -- speed and realism. Our renderer incorporates environmental lighting and spatially-varying material models to efficiently approximate light transport, either through direct estimation or via spherical basis functions. Compared to more advanced physics-based differentiable renderers leveraging path tracing, DIBR++ is highly performant due to its compact and expressive shading model, which enables easy integration with learning frameworks for geometry, reflectance and lighting prediction from a single image without requiring any ground-truth. We experimentally demonstrate that our approach achieves superior material and lighting disentanglement on synthetic and real data compared to existing rasterization-based approaches and showcase several artistic applications including material editing and relighting.

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