  Implicit neural rendering techniques have shown promising results for novel
view synthesis. However, existing methods usually encode the entire scene as a
whole, which is generally not aware of the object identity and limits the
ability to the high-level editing tasks such as moving or adding furniture. In
this paper, we present a novel neural scene rendering system, which learns an
object-compositional neural radiance field and produces realistic rendering
with editing capability for a clustered and real-world scene. Specifically, we
design a novel two-pathway architecture, in which the scene branch encodes the
scene geometry and appearance, and the object branch encodes each standalone
object conditioned on learnable object activation codes. To survive the
training in heavily cluttered scenes, we propose a scene-guided training
strategy to solve the 3D space ambiguity in the occluded regions and learn
sharp boundaries for each object. Extensive experiments demonstrate that our
system not only achieves competitive performance for static scene novel-view
synthesis, but also produces realistic rendering for object-level editing.
