A Method for Adapting Stereo Matching Algorithms to Real Environments

This study challenges the commonly used testbeds and benchmarks for testing stereo matching algorithms. Although the algorithms listed in the rankings based on these testbeds score exceptionally high, stereo matching technology still suffers from major drawbacks; as such, it is much less popular in commercial use than other technologies for 3D scanning, such as structured-light 3D scanners. One of the main problems is that the poor quality of the results is either blamed on an inappropriate stereo camera calibration or a bad stereo matching algorithm. However, this study shows that both of these steps need to be considered together. In this paper, a solution is proposed by integrating the problem of camera calibration with the execution of a stereo matching algorithm. This approach makes it possible to restore stereo matching as a technology that is competitive with other methods of 3D image acquisition.