Efficient stereo matching on embedded GPUs with zero-means cross correlation
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
Cite
@article{arxiv.2212.00476,
title = {Efficient stereo matching on embedded GPUs with zero-means cross correlation},
author = {Qiong Chang and Aolong Zha and Weimin Wang and Xin Liu and Masaki Onishi and Lei Lei and Meng Joo Er and Tsutomu Maruyama},
journal= {arXiv preprint arXiv:2212.00476},
year = {2025}
}