High Performance Stereo Vision Designed for Massively Data Parallel Platforms

Abstract--Real-time stereo vision is attractive in many applications like robot navigation and 3D scene reconstruction. Data parallel platforms, e.g. GPU, is often used for real-time stereo, because most stereo algorithms involve a large portion of data parallel computations. In this paper, we propose a stereo system on GPU which pushes the Pareto-efficiency frontline in the accuracy and speed trade-off space. Our system is based on hardware-aware algorithm design approach. The system consists of new algorithms and code optimization techniques. We emphasize on keeping the highly data parallel structure in the algorithm design process such that the algorithms can be effectively mapped to massively data parallel platforms. We propose two stereo algorithms: namely, exponential step size adaptive weight (ESAW), and exponential step size message propagation (ESMP). ESAW reduces computational complexity without sacrificing disparity accuracy. ESMP is an extension of ESAW, which incorporates ...

• Type: Journal
• Year: 2010
• Where: TCSV
• Authors: Wei Yu, Tsuhan Chen, Franz Franchetti, James C. Hoe
• Keywords: Algorithms, Computational Biology, Data Parallel, Stereo, TCSV 2010