深度学习驱动的大深度二值聚焦投影三维测量
Deep Learning-Driven Large Depth Range Three-Dimensional Measurement Using Binary Focusing Projection
刘嘉 1谈季 1王旭 1苏文青 1何昭水2
作者信息
- 1. 广东工业大学自动化学院,广东 广州 510006
- 2. 广东工业大学自动化学院,广东 广州 510006;物联网智能信息处理与系统集成教育部重点实验室,广东 广州 510006
- 折叠
摘要
二值离焦技术在高速动态三维测量中展现出巨大优势.然而,离焦投影模式决定了其仅能在合适离焦程度才可得到高质量测量结果,导致测量深度十分受限.为扩大测量深度,提出一种深度学习驱动的二值聚焦投影三维测量方法.利用聚焦投影策略,无须考虑图像离焦带来的条纹成像影响,从根本上克服二值离焦技术的局限性.其次,设计了两阶段深度学习框架对二值条纹进行处理:其中对抗式学习实现全测量深度内的高质量正弦条纹生成;分支残差学习输出条纹级数辅助相位展开,减小传统离焦投影引起的级数边缘误差.实验结果表明,所提方法可显著扩大测量深度范围,且保证全范围高质量三维重建结果.
Abstract
The binary defocusing technique(BDT)is advantageous in high-speed dynamic three-dimensional(3D)measurement.However,its depth range is limited,since the defocused projection mode determines that it can only obtain high-quality measurement results when the defocusing degree is appropriate.To expand the measurement depth,this study proposes a deep learning-driven binary focusing projection 3D measurement method.The proposed method does not necessarily consider the influence of defocusing degree,thus overcoming the limitations of BDT.Furthermore,a two-stage deep learning framework is designed to process binary fringes.In this framework,the adversarial learning realizes the generation of high-quality sinusoidal fringes in the whole depth range.Moreover,the branch residual learning outputs fringe orders to assist phase unwrapping,reducing the edge jump error caused by the traditional BDT.The experimental results show that the proposed method significantly expands the measurement depth range while maintaining high-quality 3D reconstruction in the whole depth range.
关键词
三维测量/二值投影/结构光/深度学习Key words
three-dimensional measurement/binary projection/structured light/deep learning引用本文复制引用
基金项目
国家重点研发计划(2022YFB4703100)
国家自然科学基金(62203121)
国家自然科学基金(62273105)
出版年
2024
NETL
NSTL
万方数据