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 propose a real-time high-quality stereo vision system in field-programmable gate array (FPGA). Using absolute difference-census cost initialization, cross-based cost aggregation, and semiglobal optimization, the system provides high-quality depth results for high-definition images. This is the first complete real-time hardware system that supports both cost aggregation on variable support regions and semiglobal optimization in FPGAs. Furthermore, the system is designed to be scaled with image resolution, disparity range, and parallelism degree for maximum parallel efficiency. We present the depth map quality on the Middlebury benchmark and some real-world scenarios with different image resolutions. The results show that our system performs the best among FPGA-based stereo vision systems and its accuracy is comparable with those of current top-performing software implementations. The first version of the system was demonstrated on an Altera Stratix-IV FPGA board, processing 1024 × 768 pixel images with 96 disparity levels at 67 frames/s. The system is then scaled up on a new Altera Stratix-V FPGA and the processing ability is enhanced to 1600 × 1200 pixel images with 128 disparity levels at 42 frames/s.