  Learning object-centric representations of multi-object scenes is a promising
approach towards machine intelligence, facilitating high-level reasoning and
control from visual sensory data. However, current approaches for unsupervised
object-centric scene representation are incapable of aggregating information
from multiple observations of a scene. As a result, these "single-view" methods
form their representations of a 3D scene based only on a single 2D observation
(view). Naturally, this leads to several inaccuracies, with these methods
falling victim to single-view spatial ambiguities. To address this, we propose
The Multi-View and Multi-Object Network (MulMON) -- a method for learning
accurate, object-centric representations of multi-object scenes by leveraging
multiple views. In order to sidestep the main technical difficulty of the
multi-object-multi-view scenario -- maintaining object correspondences across
views -- MulMON iteratively updates the latent object representations for a
scene over multiple views. To ensure that these iterative updates do indeed
aggregate spatial information to form a complete 3D scene understanding, MulMON
is asked to predict the appearance of the scene from novel viewpoints during
training. Through experiments, we show that MulMON better-resolves spatial
ambiguities than single-view methods -- learning more accurate and disentangled
object representations -- and also achieves new functionality in predicting
object segmentations for novel viewpoints.
