  As part of human core knowledge, the representation of objects is the
building block of mental representation that supports high-level concepts and
symbolic reasoning. While humans develop the ability of perceiving objects
situated in 3D environments without supervision, models that learn the same set
of abilities with similar constraints faced by human infants are lacking.
Towards this end, we developed a novel network architecture that simultaneously
learns to 1) segment objects from discrete images, 2) infer their 3D locations,
and 3) perceive depth, all while using only information directly available to
the brain as training data, namely: sequences of images and self-motion. The
core idea is treating objects as latent causes of visual input which the brain
uses to make efficient predictions of future scenes. This results in object
representations being learned as an essential byproduct of learning to predict.
