  We study the problem of unsupervised physical object discovery. While
existing frameworks aim to decompose scenes into 2D segments based off each
object's appearance, we explore how physics, especially object interactions,
facilitates disentangling of 3D geometry and position of objects from video, in
an unsupervised manner. Drawing inspiration from developmental psychology, our
Physical Object Discovery Network (POD-Net) uses both multi-scale pixel cues
and physical motion cues to accurately segment observable and partially
occluded objects of varying sizes, and infer properties of those objects. Our
model reliably segments objects on both synthetic and real scenes. The
discovered object properties can also be used to reason about physical events.
