LSE-CVCNet:一个基于局部结构和多尺度融合的通用立体匹配网络
Wenbang Yang1,2, Yong Zhao1,3, Ye Gu4
1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
本研究介绍了LSE-CVCNet,这是一个新的立体声匹配网络,可以改善动态场景中的差异估计. 它的创新方法提高了各种条件的准确性和概括性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 动态场景对立体匹配具有挑战,原因是特征错位和遮蔽.
- 现有的方法在复杂环境中扎着纹理变化和上下文模两可.
研究的目的:
- 开发一个新的立体声匹配网络,LSE-CVCNet,以提高动态场景中的性能.
- 解决差异估计中的特征错位和上下文模糊性的挑战.
主要方法:
- 引入了局部结构 (LSE) 以适应性注意,以差异地图的不确定性为指导.
- 开发了一种交叉图像注意力机制 (CIAM-T),用于不对称的特征提取和改进对齐.
- 实现多分辨率成本体积融合 (MRCV-F) 以维护不同尺度的细粒度细节.
主要成果:
- 伦敦证券交易所-CVCNet表现出卓越的差异估计准确性和跨领域的概括性.
- 网络在不同的照明,遮蔽和复杂几何形状下表现出稳健性.
- 在多个数据集上超越了最先进的方法,通过废弃和跨领域测试进行验证.
结论:
- 在动态环境中,LSE-CVCNet建立了适应式立体声匹配的新范式.
- 集成模块有效地提高了特征对齐和细节保存.
- 拟议的方法显示了需要强大的立体视觉的现实应用的巨大潜力.
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