  A compositional understanding of the world in terms of objects and their
geometry in 3D space is considered a cornerstone of human cognition.
Facilitating the learning of such a representation in neural networks holds
promise for substantially improving labeled data efficiency. As a key step in
this direction, we make progress on the problem of learning 3D-consistent
decompositions of complex scenes into individual objects in an unsupervised
fashion. We introduce Object Scene Representation Transformer (OSRT), a
3D-centric model in which individual object representations naturally emerge
through novel view synthesis. OSRT scales to significantly more complex scenes
with larger diversity of objects and backgrounds than existing methods. At the
same time, it is multiple orders of magnitude faster at compositional rendering
thanks to its light field parametrization and the novel Slot Mixer decoder. We
believe this work will not only accelerate future architecture exploration and
scaling efforts, but it will also serve as a useful tool for both
object-centric as well as neural scene representation learning communities.
