SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images

Understand

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.