  Neural scene representations, both continuous and discrete, have recently
emerged as a powerful new paradigm for 3D scene understanding. Recent efforts
have tackled unsupervised discovery of object-centric neural scene
representations. However, the high cost of ray-marching, exacerbated by the
fact that each object representation has to be ray-marched separately, leads to
insufficiently sampled radiance fields and thus, noisy renderings, poor
framerates, and high memory and time complexity during training and rendering.
Here, we propose to represent objects in an object-centric, compositional scene
representation as light fields. We propose a novel light field compositor
module that enables reconstructing the global light field from a set of
object-centric light fields. Dubbed Compositional Object Light Fields (COLF),
our method enables unsupervised learning of object-centric neural scene
representations, state-of-the-art reconstruction and novel view synthesis
performance on standard datasets, and rendering and training speeds at orders
of magnitude faster than existing 3D approaches.
