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

arXiv: https://arxiv.org/abs/2212.00476 · HTML: https://mlsanity.com/paper/2212.00476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Real-time self-adaptive deep stereo](https://mlsanity.com/paper/1810.05424/) (2018)
- [Anytime Stereo Image Depth Estimation on Mobile Devices](https://mlsanity.com/paper/1810.11408/) (2018)
- [Real-Time Semantic Stereo Matching](https://mlsanity.com/paper/1910.00541/) (2019)
- [On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach](https://mlsanity.com/paper/1803.09719/) (2018)
- [Embedded real-time stereo estimation via Semi-Global Matching on the GPU](https://mlsanity.com/paper/1610.04121/) (2016)
- [Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC](https://mlsanity.com/paper/1907.07745/) (2019)

## Built on by (most cited first, 1 of 1)

- [StereoVAE: A lightweight stereo-matching system using embedded GPUs](https://mlsanity.com/paper/2305.11566/) (2023)

## Abstract (arXiv)

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