  One major goal of vision is to infer physical models of objects, surfaces,
and their layout from sensors. In this paper, we aim to interpret indoor scenes
from one RGBD image. Our representation encodes the layout of walls, which must
conform to a Manhattan structure but is otherwise flexible, and the layout and
extent of objects, modeled with CAD-like 3D shapes. We represent both the
visible and occluded portions of the scene, producing a complete 3D parse. Such
a scene interpretation is useful for robotics and visual reasoning, but
difficult to produce due to the well-known challenge of segmentation, the high
degree of occlusion, and the diversity of objects in indoor scene. We take a
data-driven approach, generating sets of potential object regions, matching to
regions in training images, and transferring and aligning associated 3D models
while encouraging fit to observations and overall consistency. We demonstrate
encouraging results on the NYU v2 dataset and highlight a variety of
interesting directions for future work.
