Real-time high-quality stereo vision system in FPGA

Abstract

Stereo vision is a well-known technique for acquiring depth information. In this paper, we present an FPGA-based real-time high-quality stereo vision system. By using AD-Census cost initialization, cross-based aggregation and semi-global optimization, the system provides high-quality depth results for highdefinition images. This is the first complete real-time hardware system that supports both cost aggregation on cross-based regions and semi-global optimization on FPGA. The system can adjust image resolution, parallelism degree, and support region size to achieve maximum efficiency flexibly during the implementation. We test the accuracy of the system on the Middlebury benchmark and some real-world scenarios with different image resolutions. The results show the accuracy is among the best of FPGA-based stereo vision systems and competitive with current top-performing software implementations. We demonstrate the system using an Altera Stratix-IV FPGA board, processing 1024 × 768 pixel images at 30 frames per second.