SAD-Based Stereo Vision Machine on a System-on-Programmable-Chip (SoPC)

Xiang Zhang,Zhangwei Chen

doi:10.3390/s130303014

Xiang Zhang, Zhangwei Chen

Open Access

https://doi.org/10.3390/s130303014

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Abstract

This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC) architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users' configuration data. The Sum of Absolute Differences (SAD) algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels.

Highlights

The major task of a stereo vision system is to reconstruct the 3D representation of the scene from the 2D images captured by those cameras which are fixed with their optical axes parallel and separated by a certain distance
Disparity Computation Unit (DCU) and Stereo Matching Controller (SMC) mentioned in part 4 and part 5
An efficient hardware implementation of a real-time stereo matching processing machine is proposed by using an FPGA for the calculation of disparity maps

Summary

Introduction

The major task of a stereo vision system is to reconstruct the 3D representation of the scene from the 2D images captured by those cameras which are fixed with their optical axes parallel and separated by a certain distance. Stereo matching algorithms have played an important role in stereo vision. They can be classified into either local or global methods of correspondence. The performance of local stereo matching algorithms depends to a large extent on what similarity metric is selected. Typical similarity metrics are cross-correlation (CC), the sum of absolute differences (SAD), the sum of squared differences (SSD), the census transformation (CENS), etc. SSD and SAD find correspondences by minimizing the sum of squared or that of absolute differences in WxW windows

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Results

Conclusion