快速的立体声匹配通过多分支的注意力融合
1Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China.
ISA transactions
|May 30, 2025
概括
本研究介绍了MAF-Stereo,这是一种新的立体声匹配方法,可以通过多分支注意力融合模块来增强功能恢复. 对于自动驾驶等计算机视觉任务,MAF-Stereo实现了卓越的速度和准确性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 立体声匹配对于自动驾驶和3D重建至关重要.
- 传统方法平衡准确性和速度,而快速方法往往会牺牲精度.
研究的目的:
- 为了解决快速立体相匹配方法中功能信息不足的局限性.
- 改进功能分辨率恢复和成本量生成,以提高准确性和速度.
主要方法:
- 提出了多分支注意力融合 (MAF) 模块,以利用浅层特征进行更好的分辨率恢复.
- 整合了成本量生成中的共因相似性,以改善特征相关性.
- 开发了MAF-Stereo,一种新的立体声匹配方法.
主要成果:
- 在场景流数据集上,MAF-Stereo实现了0.57和41ms的结论速度的终点误差 (EPE).
- 在KITTI 2012年和2015年数据集上,在速度和准确性方面超过了现有的快速匹配方法.
结论:
- MAF-Stereo为快速准确的立体声匹配提供了一种有效和强大的解决方案.
- 拟议的MAF模块和辅数相似性集成显著提高了性能.
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