  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.
