Stereo Without Disparity Gradient Smoothing: a Bayesian Sensor Fusion Solution

A maximum likelihood stereo algorithm is presented that avoids the need for smoothing based on disparity gradients, provided that the common uniqueness and monotonic ordering constraints are applied. A dynamic programming algorithm allows matching of the two epipolar lines of length N and M respectively in O(N M) time and in O(N) time if a disparity limit is set. The stereo algorithm is independent of the matching primitives. A high percentage of correct matches and little smearing of depth discontinuities is obtained based on matching individual pixel intensities. Because feature extraction and windowing are unnecessary, a very fast implementation is possible.

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