Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 353-360, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
23 Feb 2017
E. Karkalou, C. Stentoumis, and G. Karras Laboratory of Photogrammetry, National Technical University of Athens, 15780 Athens, Greece
Keywords: Disparity, Stereo, Matching, Semi-global, Penalties, Adaptive, Aggregation, Optimization Abstract. 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 self-adjusting penalties is proposed: SGM penalties can be estimated solely from the statistical properties of the initial disparity space image. 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.
Conference paper (PDF, 1743 KB)

Citation: Karkalou, E., Stentoumis, C., and Karras, G.: SEMI-GLOBAL MATCHING WITH SELF-ADJUSTING PENALTIES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 353-360,, 2017.

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