Title: Indoor Frame Recovery from Refined Line Segments,
Abstract: An important yet challenging problem in understanding indoor scene is
recovering indoor frame structure from a monocular image. It is more difficult
when occlusions and illumination vary, and object boundaries are weak. To
overcome these difficulties, a new approach based on line segment refinement
with two constraints is proposed. First, the line segments are refined by four
consecutive operations, i.e., reclassifying, connecting, fitting, and voting.
Specifically, misclassified line segments are revised by the reclassifying
operation, some short line segments are joined by the connecting operation, the
undetected key line segments are recovered by the fitting operation with the
help of the vanishing points, the line segments converging on the frame are
selected by the voting operation. Second, we construct four frame models
according to four classes of possible shooting angles of the monocular image,
the natures of all frame models are introduced via enforcing the cross ratio
and depth constraints. The indoor frame is then constructed by fitting those
refined line segments with related frame model under the two constraints, which
jointly advance the accuracy of the frame. Experimental results on a collection
of over 300 indoor images indicate that our algorithm has the capability of
recovering the frame from complex indoor scenes.