Comparative analysis of local binocular and trinocular depth estimation approaches In this paper, we present a comparative analysis of local trinocular and binocular depth estimation techniques. Local techniques are chosen because of their higher computational efficiency compared to global approaches. Our aim is to quantify the benefits of the third camera with respect to performance and computational burden. We have adopted the colorweighted localwindow approach in stereo matching, where pixels within local spatial window around the pixel being processed are penalized by their colors in order to ensure better adaptivity to local structures. Thus, the window size becomes the main parameter which influences the quality and determines the execution time. Extensive experiments on large set of data have been carried out to test trinocular versus binocular setting in terms of quality of estimated depth and execution time. Both natural and artificial scenes have been tested. A set of quality measures has been used to support the comparisons. MPEG Depth Estimation Reference Software has been used as a reference benchmark as well. Results show that from some window size on, the trinocular setting outperforms the binocular in general: providing higher quality for less computational time. While comparisons were done for u0027pureu0027 depth estimation, we also run postprocessing on depth estimates in order to analyze the potential of estimated depths to be further improved.

Symmetric trinocular dense disparity estimation for car surrounding camera array This paper presented a novel dense disparity estimation method which is called as symmetric trinocular dense disparity estimation. Also a car surrounding camera array application is proposed to improve the driving safety by the proposed symmetric trinocular dense disparity estimation algorithm. The symmetric trinocular property is conducted to show the benefit of doing disparity estimation with three cameras. A 1D fast search algorithm is described to speed up the slowness of the original full search algorithms. And the ID fast search algorithm utilizes the horizontal displacement property of the cameras to further check the correctness of the disparity vector. The experimental results show that the symmetric trinocular property improves the quality and smoothness of the disparity vector.