中国光学（中英文）2024，Vol.17Issue(1)：118-127,10.DOI:10.37188/CO.2023-0066
基于轻型自限制注意力的结构光相位及深度估计混合网络
A hybrid network based on light self-limited attention for structured light phase and depth estimation
摘要
Abstract
Phase retrieval and depth estimation are vital to three-dimensional measurement using structured light.Currently,conventional methods for structured light phase retrieval and depth estimation have limited efficiency and are lack of robustness in their results and so on.To improve the reconstruction effect of struc-tured light by deep learning,we propose a hybrid network for structured light phase and depth estimation based on Light Self-Limited Attention(LSLA).Specifically,a CNN-Transformer hybrid module is construc-ted and integrated into a U-shaped structure to realize the advantages complementary of CNN and Trans-former.The proposed network is experimentally compared with other networks in structured light phase es-timation and structured light depth estimation.The experimental results indicate that the proposed network achieves finer detail processing in phase and depth estimation compared to other networks.Specifically,for structured light phase and depth estimation,its accuracy improves by 31%and 26%,respectively.Therefore,the proposed network improves the accuracy of deep neural networks in the structured light phase and depth estimation areas.关键词
结构光/深度学习/自限制注意力/相位估计/深度估计Key words
structured light/deep learning/self-limited attention/phase estimation/depth estimation分类
信息技术与安全科学引用本文复制引用
朱新军,赵浩淼,王红一,宋丽梅,孙瑞群..基于轻型自限制注意力的结构光相位及深度估计混合网络[J].中国光学（中英文）,2024,17(1):118-127,10.基金项目
国家自然科学基金(No.61905178) （No.61905178）
天津市教委科研计划项目(No.2019KJ021)Supported by National Natural Science Foundation of China(No.61905178) （No.2019KJ021）
Science&Technology Develop-ment Fund of Tianjin Education Commission for Higher Education(No.2019KJ021) （No.2019KJ021）
