This paper describes an improvement to the dynamic programming approach for dense stereo. Traditionally dense stereo algorithms proceed independently for each pair of epipolar lines and then a further step is used to smooth the esti- mated disparities between the epipolar lines. This typically results in a streaky disparity map along depth discontinuities. In order to overcome this problem the information from corner and edge matching algorithms are exploited. Indeed we present a unified dynamic programming/statistical framework that allows the incorporation of any partial knowledge about disparities, such as matched features and known surfaces within the scene. The result is a fully automatic dense stereo system with a faster run time and greater accuracy than the standard dynamic programming method.

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In  Image and Vision Computing (IVC)

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