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Discovering 3D arrangements of objects from single indoor images is importantgiven its many applications including interior design, content creation, etc.Although heavily researched in the recent years, existing approaches break downunder medium or heavy occlusion as the core object detection module startsfailing in absence of directly visible cues. Instead, we take into accountholistic contextual 3D information, exploiting the fact that objects in indoorscenes co-occur mostly in typical near-regular configurations. First, we use aneural network trained on real indoor annotated images to extract 2D keypoints,and feed them to a 3D candidate object generation stage. Then, we solve aglobal selection problem among these 3D candidates using pairwise co-occurrencestatistics discovered from a large 3D scene database. We iterate the processallowing for candidates with low keypoint response to be incrementally detectedbased on the location of the already discovered nearby objects. Focusing onchairs, we demonstrate significant performance improvement over combinations ofstate-of-the-art methods, especially for scenes with moderately to severelyoccluded objects.

SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images