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An important yet challenging problem in understanding indoor scene isrecovering indoor frame structure from a monocular image. It is more difficultwhen occlusions and illumination vary, and object boundaries are weak. Toovercome these difficulties, a new approach based on line segment refinementwith two constraints is proposed. First, the line segments are refined by fourconsecutive operations, i.e., reclassifying, connecting, fitting, and voting.Specifically, misclassified line segments are revised by the reclassifyingoperation, some short line segments are joined by the connecting operation, theundetected key line segments are recovered by the fitting operation with thehelp of the vanishing points, the line segments converging on the frame areselected by the voting operation. Second, we construct four frame modelsaccording to four classes of possible shooting angles of the monocular image,the natures of all frame models are introduced via enforcing the cross ratioand depth constraints. The indoor frame is then constructed by fitting thoserefined line segments with related frame model under the two constraints, whichjointly advance the accuracy of the frame. Experimental results on a collectionof over 300 indoor images indicate that our algorithm has the capability ofrecovering the frame from complex indoor scenes.

Indoor Frame Recovery from Refined Line Segments