实时和精确的立体声匹配通过三融合音量立体视觉立体视觉
Anze Xu1, Lin Yang1, Jingzhong Li1
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; National Engineering Research Centre for Automotive Power and Intelligent Control, Shanghai, China.
概括
本研究引入了实时立体声匹配的三融合体积 (TFV),通过融合纹理和相似性信息来提高准确性. 新的TFV模块和TCMNet模型显著提高了关键基准的表现.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 实时立体声匹配需要高效和准确的成本量.
- 现有的方法在具有挑战性的区域中难以保留纹理细节.
- 目前的方法往往缺乏适应机制来匹配像素对.
研究的目的:
- 提出三融合体积 (TFV) 以在立体相匹配中有效地融合纹理和相似信息.
- 引入一种新的方法,在不良位置的区域保存纹理细节.
- 根据TFV框架开发一个实时和准确的立体声匹配模型,TCMNet.
主要方法:
- 通过整合纹理,相关性和相互体积,开发了三融合体积 (TFV).
- 引入了使用低级特征进行细节恢复的纹理体积.
- 整合可变形的注意力在相关性体积中进行自适应性像素匹配.
- 提出了一种利用相互信息进行概率分布相似性的相互交易量.
主要成果:
- TFV模块显著提高了现有的实时立体声匹配方法的性能.
- 拟议的TCMNet模型在Scene Flow,KITTI-2012和KITTI-2015基准上表现出领先的表现.
- TFV有效地融合了纹理细节和相似性信息,提高了准确性.
结论:
- TFV是一个轻量级且有效的模块,用于改进实时立体声匹配.
- TCMNet代表了一个最先进的模型,用于准确和高效的立体声匹配.
- 提出的方法在立体视觉领域提供了显著的进步.
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