SEMI-GLOBAL MATCHING WITH SELF-ADJUSTING PENALTIES
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.