Efficient stereo matching on embedded GPUs with zero-means cross correlation

TL;DR

This paper introduces a fast, energy-efficient stereo matching method optimized for embedded GPUs, achieving real-time performance and improved accuracy on mobile platforms by accelerating the ZNCC algorithm with a zigzag scanning technique.

Contribution

It proposes a novel acceleration technique for ZNCC on embedded GPUs, enabling real-time stereo matching with higher accuracy and lower computational cost.

Findings

2x faster than traditional ZNCC methods

26% faster than latest NCC methods

Real-time processing at 32 fps on Jetson Tx2

Abstract

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel's computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1,280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed. Source Code: https://github.com/changqiong/Z2ZNCC.git