  The visual world can be parsimoniously characterized in terms of distinct
entities with sparse interactions. Discovering this compositional structure in
dynamic visual scenes has proven challenging for end-to-end computer vision
approaches unless explicit instance-level supervision is provided. Slot-based
models leveraging motion cues have recently shown great promise in learning to
represent, segment, and track objects without direct supervision, but they
still fail to scale to complex real-world multi-object videos. In an effort to
bridge this gap, we take inspiration from human development and hypothesize
that information about scene geometry in the form of depth signals can
facilitate object-centric learning. We introduce SAVi++, an object-centric
video model which is trained to predict depth signals from a slot-based video
representation. By further leveraging best practices for model scaling, we are
able to train SAVi++ to segment complex dynamic scenes recorded with moving
cameras, containing both static and moving objects of diverse appearance on
naturalistic backgrounds, without the need for segmentation supervision.
Finally, we demonstrate that by using sparse depth signals obtained from LiDAR,
SAVi++ is able to learn emergent object segmentation and tracking from videos
in the real-world Waymo Open dataset.
