Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Computer vision, augmented reality, and virtual reality <s> Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web. <s> BIB001 </s> Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Computer vision, augmented reality, and virtual reality <s> Many vision applications require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most real-time stereo applications rely on local winner-takes-all optimization in the disparity computation process. These local approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that, through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate whether these cost aggregation approaches can be adopted in real-time stereo applications and, if so, how well they perform under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing power of programmable graphics hardware. Six recent cost aggregation approaches are implemented and optimized for graphics hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing speed and result quality are reported. <s> BIB002 </s> Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Computer vision, augmented reality, and virtual reality <s> In the last decades several cost aggregation methods aimed at improving the robustness of stereo correspondence within local and global algorithms have been proposed. Given the recent developments and the lack of an appropriate comparison, in this paper we survey, classify and compare experimentally on a standard data set the main cost aggregation approaches proposed in literature. The experimental evaluation addresses both accuracy and computational requirements, so as to outline the best performing methods under these two criteria. <s> BIB003 </s> Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms <s> Computer vision, augmented reality, and virtual reality <s> Stereo matching techniques aim at reconstructing disparity maps from a pair of images. The use of stereo matching techniques in embedded systems is very challenging due to the complexity of the state-of-the-art algorithms. An efficient local stereo matching algorithm has been chosen from the literature and implemented on a c6678 DSP. Arithmetic simplifications such as approximation by piecewise linear functions and fixed point conversions are proposed. Thanks to factorisation and precomputing, the memory footprint is reduced by a factor 13 to fit on the memory footprint available on embedded systems. A 14.5 fps speed (factor 60 speed-up) has been reached with a small quality loss on the final disparity map. <s> BIB004
Many applications in computer vision, augmented reality, and virtual reality are compute-intensive. As a result, many of their algorithms (e.g., stereo matching algorithms) have always been implemented with quality versus efficiency tradeoffs in mind BIB002 BIB001 BIB003 . The implementations of these algorithms have used techniques including fixed-point implementations of expensive floating-point numerics BIB004 and algorithmic approximations such as removing time-consuming backtracking steps  when implementing these algorithms on FPGA accelerators.