  Representing scenes at the granularity of objects is a prerequisite for scene
understanding and decision making. We propose PriSMONet, a novel approach based
on Prior Shape knowledge for learning Multi-Object 3D scene decomposition and
representations from single images. Our approach learns to decompose images of
synthetic scenes with multiple objects on a planar surface into its constituent
scene objects and to infer their 3D properties from a single view. A recurrent
encoder regresses a latent representation of 3D shape, pose and texture of each
object from an input RGB image. By differentiable rendering, we train our model
to decompose scenes from RGB-D images in a self-supervised way. The 3D shapes
are represented continuously in function-space as signed distance functions
which we pre-train from example shapes in a supervised way. These shape priors
provide weak supervision signals to better condition the challenging overall
learning task. We evaluate the accuracy of our model in inferring 3D scene
layout, demonstrate its generative capabilities, assess its generalization to
real images, and point out benefits of the learned representation.
