  Learning neural radiance fields of a scene has recently allowed realistic
novel view synthesis of the scene, but they are limited to synthesize images
under the original fixed lighting condition. Therefore, they are not flexible
for the eagerly desired tasks like relighting, scene editing and scene
composition. To tackle this problem, several recent methods propose to
disentangle reflectance and illumination from the radiance field. These methods
can cope with solid objects with opaque surfaces but participating media are
neglected. Also, they take into account only direct illumination or at most
one-bounce indirect illumination, thus suffer from energy loss due to ignoring
the high-order indirect illumination. We propose to learn neural
representations for participating media with a complete simulation of global
illumination. We estimate direct illumination via ray tracing and compute
indirect illumination with spherical harmonics. Our approach avoids computing
the lengthy indirect bounces and does not suffer from energy loss. Our
experiments on multiple scenes show that our approach achieves superior visual
quality and numerical performance compared to state-of-the-art methods, and it
can generalize to deal with solid objects with opaque surfaces as well.
