LOCK-FREE MULTITHREADED SEMI-GLOBAL MATCHING WITH AN ARBITRARY NUMBER OF PATH DIRECTIONS

This paper describes an efficient implementation of the semi-global matching (SGM) algorithm on multi-core processors that allows a nearly arbitrary number of path directions for the cost aggregation stage. The scanlines for each orientation are discretized iteratively once, and the regular substructures of the obtained template are reused and shifted to concurrently sum up the path cost in at most two sweeps per direction over the disparity space image. Since path overlaps do not occur at any time, no expensive thread synchronization will be needed. To further reduce the runtime on high counts of path directions, pixel-wise disparity gating is applied, and both the cost function and disparity loop of SGM are optimized using current single instruction multiple data (SIMD) intrinsics for two major CPU architectures. Performance evaluation of the proposed implementation on synthetic ground truth reveals a reduced height error if the number of aggregation directions is significantly increased or when the paths start with an angular offset. Overall runtime shows a speedup that is nearly linear to the number of available processors.

SEMI-GLOBAL MATCHING WITH SELF-ADJUSTING PENALTIES

The demand for 3D models of various scales and precisions is strong for a wide range of applications, among which cultural heritage recording is particularly important and challenging. In this context, dense image matching is a fundamental task for processes which involve image-based reconstruction of 3D models. Despite the existence of commercial software, the need for complete and accurate results under different conditions, as well as for computational efficiency under a variety of hardware, has kept image-matching algorithms as one of the most active research topics. Semi-global matching (SGM) is among the most popular optimization algorithms due to its accuracy, computational efficiency, and simplicity. A challenging aspect in SGM implementation is the determination of smoothness constraints, i.e. penalties P1, P2 for disparity changes and discontinuities. In fact, penalty adjustment is needed for every particular stereo-pair and cost computation. In this work, a novel formulation of <i>self-adjusting penalties</i> is proposed: <i>SGM penalties can be estimated solely from the statistical properties of the initial disparity space image</i>. The proposed method of self-adjusting penalties (SGM-SAP) is evaluated using typical cost functions on stereo-pairs from the recent Middlebury dataset of interior scenes, as well as from the EPFL Herz-Jesu architectural scenes. Results are competitive against the original SGM estimates. The significant aspects of self-adjusting penalties are: (i) the time-consuming tuning process is avoided; (ii) SGM can be used in image collections with limited number of stereo-pairs; and (iii) no heuristic user intervention is needed.

A Improved Stereo Matching Fast Algorithm Based on Dynamic Programming

Compared with the local algorithm in stereo matching, the high quality disparity space image is calculated by the global algorithm, which is difficult to use in practical application for its long computation time. The dynamic programming is one of the global algorithms with a fast matching speed, but it has strip blemish in matching result. In this paper, a new dynamic programming based method is proposed to accelerate the matching speed and improve the matching quality. Firstly, the color feature of two images are calculated using the Laplacians of Gaussian pyramid algorithm, and the color feature of the image pair obtained are matched. Secondly, the matching points are taken as the ground control points of the scan line, which is cut into several short line segments. Finally, all line segments are matched to obtain the disparity of the scan line. The experimental results show that the matching speed is accelerated greatly with improved disparity image quality