  We present neural architectures that disentangle RGB-D images into objects'
shapes and styles and a map of the background scene, and explore their
applications for few-shot 3D object detection and few-shot concept
classification. Our networks incorporate architectural biases that reflect the
image formation process, 3D geometry of the world scene, and shape-style
interplay. They are trained end-to-end self-supervised by predicting views in
static scenes, alongside a small number of 3D object boxes. Objects and scenes
are represented in terms of 3D feature grids in the bottleneck of the network.
We show that the proposed 3D neural representations are compositional: they can
generate novel 3D scene feature maps by mixing object shapes and styles,
resizing and adding the resulting object 3D feature maps over background scene
feature maps. We show that classifiers for object categories, color, materials,
and spatial relationships trained over the disentangled 3D feature sub-spaces
generalize better with dramatically fewer examples than the current
state-of-the-art, and enable a visual question answering system that uses them
as its modules to generalize one-shot to novel objects in the scene.
