원문정보
초록
영어
Stereo matching is one of the key technologies in stereo vision system due to its ultra high data bandwidth requirement, heavy memory accessing and algorithm complexity. To speed up stereo matching, various algorithms are implemented by different software and hardware processing methods. This paper presents a survey of stereo matching software and hardware implementation research status based on local and global algorithm analysis. Based on different processing platforms, including CPU, DSP, GPU, FPGA and ASIC, analysis are made on software or hardware realization performance, which is represented by frame rate, efficiency represented by MDES, and processing quality represented by error rate. Among them, GPU, FPGA and ASIC implementations are suitable for real-time embedded stereo matching applications, because they are low power consumption, low cost, and have high performance. Finally, further stereo matching optimization technologies are pointed out, including both algorithm and parallelism optimization for data bandwidth reduction and memory storage strategy.
목차
1. Introduction
2. Stereo Matching Algorithms Overview
2.1. BP Method
2.2. GC Method
2.3. DP Method
2.4. Window Based Matching
2.5. Affine Transformation Method
2.6. Other Intelligence Method
3. Software and Hardware Processing of Stereo Matching Methods
3.1. CPU Implementation
3.2. DSP Implementation
3.3. GPU Implementation
3.4. FPGA/ASIC Implementation
4. Software and Hardware Processing Method Comparison
5. Future Research Direction
5.1. Image Segmentation or Hierarchy Optimization
5.2. Occlusion and Consistency Handling
5.3. Matching Cost & Energy Optimization Improvement
5.4. Cooperative Optimization
5.5. Efficient Memory Arrangement Method
5.6. Advanced VLSI design Method
6. Conclusion
Acknowledgements
References