  Discovering 3D arrangements of objects from single indoor images is important
given its many applications including interior design, content creation, etc.
Although heavily researched in the recent years, existing approaches break down
under medium or heavy occlusion as the core object detection module starts
failing in absence of directly visible cues. Instead, we take into account
holistic contextual 3D information, exploiting the fact that objects in indoor
scenes co-occur mostly in typical near-regular configurations. First, we use a
neural network trained on real indoor annotated images to extract 2D keypoints,
and feed them to a 3D candidate object generation stage. Then, we solve a
global selection problem among these 3D candidates using pairwise co-occurrence
statistics discovered from a large 3D scene database. We iterate the process
allowing for candidates with low keypoint response to be incrementally detected
based on the location of the already discovered nearby objects. Focusing on
chairs, we demonstrate significant performance improvement over combinations of
state-of-the-art methods, especially for scenes with moderately to severely
occluded objects.
