  Recent advances in implicit neural representations and differentiable
rendering make it possible to simultaneously recover the geometry and materials
of an object from multi-view RGB images captured under unknown static
illumination. Despite the promising results achieved, indirect illumination is
rarely modeled in previous methods, as it requires expensive recursive path
tracing which makes the inverse rendering computationally intractable. In this
paper, we propose a novel approach to efficiently recovering spatially-varying
indirect illumination. The key insight is that indirect illumination can be
conveniently derived from the neural radiance field learned from input images
instead of being estimated jointly with direct illumination and materials. By
properly modeling the indirect illumination and visibility of direct
illumination, interreflection- and shadow-free albedo can be recovered. The
experiments on both synthetic and real data demonstrate the superior
performance of our approach compared to previous work and its capability to
synthesize realistic renderings under novel viewpoints and illumination. Our
code and data are available at https://zju3dv.github.io/invrender/.
