We study the problem of generating plausible interpretations of a scene from a collection of line segments automatically extracted from a single indoor image. We show that we can recognize the three dimensional structure of the interior of a building, even in the presence of occluding objects. Several physically valid structure hypotheses are proposed by geometric reasoning and verified to find the best fitting model to line segments, which is then converted to a full 3D model. Our experiments demonstrate that our structure recovery from line segments is comparable with methods using full image appearance. Our approach shows how a set of rules describing geometric constraints between groups of segments can be used to prune scene interpretation hypotheses and to generate the most plausible interpretation.
Later works REF by Lee et al. try to generate plausible interpretations of a scene from a collection of line segments automatically extracted from a single indoor image.
Geometric reasoning for single image structure recovery
2009 IEEE Conference on Computer Vision and Pattern Recognition
2009 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science