Layout Estimation of Highly Cluttered Indoor Scenes Using Geometric and Semantic Cues

Recovering the spatial layout of cluttered indoor scenes is a challenging problem. Current methods generate layout hypotheses from vanishing point estimates produced using 2D image features. This method fails in highly cluttered scenes in which most of the image features come from clutter instead of the room’s geometric structure. In this paper, we propose to use human detections as cues to more accurately estimate the vanishing points. Our method is built on top of the fact that people are often the focus of indoor scenes, and that the scene and the people within the scene should have consistent geometric configurations in 3D space. We contribute a new data set of highly cluttered indoor scenes containing people, on which we provide baselines and evaluate our method. This evaluation shows that our approach improves 3D interpretation of scenes.