Dense Disparity Real-Time Stereo Vision Algorithm for Resource-Limited Systems

It is evident that the accuracy of stereo vision algorithms has continued to increase based on commonly used quantitative evaluations of the resulting disparity maps. This paper focuses on the development of promising stereo vision algorithms that efficiently tradeoff accuracy for large reductions in required computational resources. An intensity profile shape-matching algorithm is introduced as an example of an algorithm that makes such tradeoffs. The proposed algorithm is compared to both a basic sum-of-absolute-differences (SAD) block-matching algorithm, as well as a stereo vision algorithm that is highly ranked for its accuracy based on the Middlebury evaluation criteria. This comparison shows that the proposed algorithm's accuracy on the commonly used Tsukuba stereo image pair is lower than many published stereo vision algorithms, but that for unrectified stereo image pairs that have even the slightest differences in brightness, it is potentially more robust than algorithms that rely on SAD block matching. An example application that requires 3-D information is implemented to show that the accuracy of the proposed algorithm is sufficient for this use. Timing results show that this is a very fast dense-disparity stereo vision algorithm when compared to other algorithms capable of running on a standard microprocessor.