high performance computing on graphics processing units: hgpu.org

Stereo matching technique is used to estimate the depth of objects in an image acquired from real time scenes. The basic algorithm is not very complex but is computationally exhaustive and hinders its usage for real time applications. However, this algorithm is highly data parallel and it highly suitable for execution on GPGPU (General-purpose graphical processing units). In this paper, we are proposing the parallel implementation of the fast stereo matching algorithm based on correlation of multi-resolution images using CUDA (Compute Unified Device Architecture). The performance of this implementation is faster than most of the software implementations of this method and comparable with FPGA implementation and few other optimized methods mentioned in the references. This enables the real time usage of stereo matching method. We have also provided performance comparison and results for different methods of stereo matching on CUDA. The paper concludes with analysis of results and the reasons of the performance variations. We have also given qualitative image data for comparison of accuracy of different stereo correspondence methods.