Calculating dense disparity maps from color stereo images, an efficient implementation

This paper presents an efficient implementation for correlation based stereo. Research in this area can roughly be divided in two classes: improving accuracy regardless of computing time and scene reconstruction in real-time. Algorithms achieving video frame rates must have strong limitations in image size and disparity search range, whereas high quality results often need several minutes per image pair. This paper tries to fill the gap, it provides instructions how to implement correlation based disparity calculation with high speed and reasonable quality that can be used in a wide range of applications or to provide an initial solution for more sophisticated methods. Left to right consistency checking and uniqueness validation are used to eliminate false matches. Optionally, a fast median filter can be applied to the results to further remove outliers. Source code will be made publicly available as contribution to the Open Source Computer Vision Library, further acceleration with SIMD instructions is planned for the near future.