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.
David C. Lee, Martial Hebert, Takeo Kanade