High-quality real-time hardware stereo matching based on guided image filtering

Abstract

Stereo matching is a vital task in several emerging embedded vision applications requiring high-quality depth computation and real-time frame-rate. Although several stereo matching dedicated-hardware systems have been proposed in recent years, only few of them focus on balancing accuracy and speed. This paper proposes a hardware-based stereo matching architecture that aims to provide high accuracy and concurrently high performance in embedded vision applications. The proposed architecture integrates a compact and efficient design of the recently proposed guided image filter; an edge-preserving filter that reduces the hardware complexity of the implemented stereo algorithm, while at the same time maintains high-quality results. A prototype of the architecture has been implemented on a Kintex-7 FPGA board, achieving 60 fps for 720p resolution images. Moreover, the proposed design delivers leading accuracy when compared to state-of-the-art hardware implementations.