  We propose an approach to predict the 3D shape and pose for the objects
present in a scene. Existing learning based methods that pursue this goal make
independent predictions per object, and do not leverage the relationships
amongst them. We argue that reasoning about these relationships is crucial, and
present an approach to incorporate these in a 3D prediction framework. In
addition to independent per-object predictions, we predict pairwise relations
in the form of relative 3D pose, and demonstrate that these can be easily
incorporated to improve object level estimates. We report performance across
different datasets (SUNCG, NYUv2), and show that our approach significantly
improves over independent prediction approaches while also outperforming
alternate implicit reasoning methods.
