  We present a method for composing photorealistic scenes from captured images
of objects. Our work builds upon neural radiance fields (NeRFs), which
implicitly model the volumetric density and directionally-emitted radiance of a
scene. While NeRFs synthesize realistic pictures, they only model static scenes
and are closely tied to specific imaging conditions. This property makes NeRFs
hard to generalize to new scenarios, including new lighting or new arrangements
of objects. Instead of learning a scene radiance field as a NeRF does, we
propose to learn object-centric neural scattering functions (OSFs), a
representation that models per-object light transport implicitly using a
lighting- and view-dependent neural network. This enables rendering scenes even
when objects or lights move, without retraining. Combined with a volumetric
path tracing procedure, our framework is capable of rendering both intra- and
inter-object light transport effects including occlusions, specularities,
shadows, and indirect illumination. We evaluate our approach on scene
composition and show that it generalizes to novel illumination conditions,
producing photorealistic, physically accurate renderings of multi-object
scenes.
