Literature Survey on Stereo Vision Disparity Map Algorithms

Table 1

Previous review papers on stereo vision disparity map algorithms.

Year	Author	Focus
2002	Scharstein and Szeliski [11]	Proposed a taxonomy for vision algorithms and provided a quality metric to compare and evaluate multiple blocks of algorithms as shown in Figure 1. They have also provided a test bed for measurable evaluation of stereo depth map algorithms. The test bed or benchmarking dataset consists of four images (Tsukuba, Venus, Teddy, and Cones) which are available at http://www.middlebury.edu/stereo.
2003	Brown et al. [19]	Reviewed advances in stereo vision disparity map algorithms regarding correspondence methods and occlusion handling methods for real time implementations.
2008	Tombari et al. [77]	Presented a survey and compared the different methods of cost aggregation for stereo correspondence through accuracy and computational requirements.
2008	Lazaros et al. [12]	Reviewed developments in stereo vision algorithms implemented via software and hardware categorized in terms of their major attributes. The comparison of local and global methods provided by previously developed algorithms implemented on software and hardware based platforms was presented in this work.
2011	Tombari et al. [101]	Contributed an evaluation of stereo vision depth map algorithms in terms of their 3D object recognition ability.
2013	Tippetts et al. [8]	Reviewed stereo vision algorithms and their suitability for resource-limited systems. They have compiled and presented an accuracy and runtime performance data for all stereo vision disparity map algorithms in the past decade with an emphasis on real time performance.