  We present a physics-based inverse rendering method that learns the
illumination, geometry, and materials of a scene from posed multi-view RGB
images. To model the illumination of a scene, existing inverse rendering works
either completely ignore the indirect illumination or model it by coarse
approximations, leading to sub-optimal illumination, geometry, and material
prediction of the scene. In this work, we propose a physics-based illumination
model that first locates surface points through an efficient refined sphere
tracing algorithm, then explicitly traces the incoming indirect lights at each
surface point based on reflection. Then, we estimate each identified indirect
light through an efficient neural network. Moreover, we utilize the Leibniz's
integral rule to resolve non-differentiability in the proposed illumination
model caused by boundary lights inspired by differentiable irradiance in
computer graphics. As a result, the proposed differentiable illumination model
can be learned end-to-end together with geometry and materials estimation. As a
side product, our physics-based inverse rendering model also facilitates
flexible and realistic material editing as well as relighting. Extensive
experiments on synthetic and real-world datasets demonstrate that the proposed
method performs favorably against existing inverse rendering methods on novel
view synthesis and inverse rendering.
