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基于视觉的三维重建关键技术研究综述

郑太雄 黄帅 李永福 冯明驰

郑太雄, 黄帅, 李永福, 冯明驰. 基于视觉的三维重建关键技术研究综述. 自动化学报, 2020, 46(4): 631-652. doi: 10.16383/j.aas.2017.c170502
引用本文: 郑太雄, 黄帅, 李永福, 冯明驰. 基于视觉的三维重建关键技术研究综述. 自动化学报, 2020, 46(4): 631-652. doi: 10.16383/j.aas.2017.c170502
ZHENG Tai-Xiong, HUANG Shuai, LI Yong-Fu, FENG Ming-Chi. Key Techniques for Vision Based 3D Reconstruction: a Review. ACTA AUTOMATICA SINICA, 2020, 46(4): 631-652. doi: 10.16383/j.aas.2017.c170502
Citation: ZHENG Tai-Xiong, HUANG Shuai, LI Yong-Fu, FENG Ming-Chi. Key Techniques for Vision Based 3D Reconstruction: a Review. ACTA AUTOMATICA SINICA, 2020, 46(4): 631-652. doi: 10.16383/j.aas.2017.c170502

基于视觉的三维重建关键技术研究综述

doi: 10.16383/j.aas.2017.c170502
基金项目: 

国家自然科学基金 61773082

国家自然科学基金 51505054

重庆市基础与前沿技术项目 cstc2018jcyjAX0684

重庆邮电大学交叉项目 A2018-02

重庆市重点产业共性关键技术创新专项项目 cstc2015zdcy-ztzx60002

详细信息
    作者简介:

    黄帅   重庆大学自动化学院博士研究生. 2018年获得重庆邮电大学硕士学位.主要研究方向为智能车环境感知, 信息物理系统, 智能交通, 数据挖掘. E-mail: huangs316@163.com

    李永福   重庆邮电大学副教授, 工学博士, 普渡大学博士后.主要研究方向为车联网与智能交通, 汽车电子, 控制理论与应用. E-mail: laf1212@163.com

    冯明驰   重庆邮电大学副教授, 工学博士.主要研究方向为多相机视觉测量. E-mail: fengmc@cqupt.edu.cn

    通讯作者:

    郑太雄   重庆邮电大学教授, 工学博士.主要研究方向为汽车电子相关研究.本文通信作者.E-mail: zhengtx@cqupt.edu.cn

  • 本文责任编委 桑农

Key Techniques for Vision Based 3D Reconstruction: a Review

Funds: 

National Natural Science Foundation of China 61773082

National Natural Science Foundation of China 51505054

Basic Science and Emerging Technology of Chongqing cstc2018jcyjAX0684

Project of Crossing and Emerging Area of CQUPT A2018-02

Chongqing Science and Technology Commission cstc2015zdcy-ztzx60002

More Information
    Author Bio:

    HUANG Shuai   Ph.D. candidate in the School of Automation, Chongqing University. He received his master degree from Chongqing University of Posts and Telecommunications in 2018. His research interest covers environment perception for intelligent vehicle, cyber-physical systems, intelligent transportation systems, and data mining

    LI Yong-Fu   Ph.D., associate professor at Chongqing University of Posts and Telecommunications. Since 2014 to 2016, Dr. Li has been worked as the Post-doc Research Associate at Purdue University, West Lafayette, IN 47906, USA. He research interest covers connected and autonomous vehicles, intelligent transportation systems, automotive electronics, control theory and application

    FENG Ming-Chi   Ph.D., associate professor at Chongqing University of Posts and Telecommunications. His research interest covers vision measurement of multi-cameras

    Corresponding author: ZHENG Tai-Xiong   Ph.D., professor at Chongqing University of Posts and Telecommunications. He research interest covers automotive electronics related research. Corresponding author of this paper
  • Recommended by SANG Nong
  • 摘要: 三维重建在视觉方面具有很高的研究价值, 在机器人视觉导航、智能车环境感知系统以及虚拟现实中被广泛应用.本文对近年来国内外基于视觉的三维重建方法的研究工作进行了总结和分析, 主要介绍了基于主动视觉下的激光扫描法、结构光法、阴影法以及TOF (Time of flight)技术、雷达技术、Kinect技术和被动视觉下的单目视觉、双目视觉、多目视觉以及其他被动视觉法的三维重建技术, 并比较和分析这些方法的优点和不足.最后对三维重建的未来发展作了几点展望.
    Recommended by SANG Nong
    1)  本文责任编委 桑农
  • 图  1  三维重建技术分类

    Fig.  1  Classification of 3D reconstruction technology

    图  2  激光扫描数据处理流程

    Fig.  2  The process of laser scanning data processing

    图  3  结构光三角测量原理示意图

    Fig.  3  Schematic diagram of the principle of structured light triangulation

    图  4  平行光阴影法

    Fig.  4  Parallel photocathode

    图  5  Kinect传感器

    Fig.  5  Kinect sensor

    图  6  基于单目视觉的三维重建流程

    Fig.  6  3D reconstruction process based on monocular vision

    图  7  双目视觉系统

    Fig.  7  Binocular vision system

    图  8  汇聚式双目视觉理论模型

    Fig.  8  Convergent binocular vision theory model

    图  9  双目视觉三维重建系统组成

    Fig.  9  The composition of the binocular vision 3D reconstruction system

    图  10  双目视觉获取深度信息流程

    Fig.  10  Process of access to depth information by binocular vision

    图  11  基于BP网络结构的三维重建

    Fig.  11  3D reconstruction based on BP network structure

    表  1  主动视觉方法对比

    Table  1  Active visual method comparison

    方法 激光扫描法[28-31] 结构光法[32-42] 阴影法[43-48] TOF技术[49-53] 雷达技术[54-58] Kinect技术[59-67]
    优点 1.重建结果很精确;
    2.能建立形状不规则物体的三维模型.
    1.简单方便、无破坏性;
    2.重建结果速率快、精度高、能耗低、抗干扰能力强.
    1.设备简单, 图像直观;
    2.密度均匀, 简单低耗, 对图像的要求非常低.
    1.数据采集频率高;
    2.垂直视场角大;
    3.可以直接提取几何信息.
    1.视场大、扫描距离远、灵敏度高、功耗低;
    2.直接获取深度信息, 不用对内部参数进行标定.
    1.价格便宜、轻便;
    2.受光照条件的影响较小;
    3.同时获取深度图像和彩色图像.
    缺点 1.需要采用算法来修补漏洞;
    2.得到的三维点云数据量非常庞大, 而且还需要对其进行配准, 耗时较长;3.价格昂贵.
    1.测量速度慢;
    2.不适用室外场景.
    1.对光照的要求较高, 需要复杂的记录装置;
    2.涉及到大口径的光学部件的消像差设计、加工和调整.
    1.深度测量系统误差大;
    2.灰度图像对比度差、分辨率低;
    3.搜索空间大、效率低;
    4.算法扩展性差, 空间利用率低.
    1.受环境的影响较大;
    2.计算量较大, 实时性较差;
    1.深度图中含有大量的噪声;
    2.对单张图像的重建效果较差.
    下载: 导出CSV

    表  2  单目、双目和多目视觉方法对比

    Table  2  Comparison of monocular, binocular and multiocular vision methods

    单目视觉[68] 双目视觉[101-110, 112] 多目视觉[111, 113-119]
    优点 1.简单方便、灵活可靠、使用范围广;
    2.可以实现重建过程中的摄像机自标定, 处理时间短;
    3.价格便宜.
    1.方法成熟;
    2.能够稳定地获得较好的重建效果;
    3.应用广泛.
    1.避免双目视觉方法中难以解决的假目标、边缘模糊及误匹配等问题;
    2.在多种条件下进行非接触、自动、在线的测量和检测;
    3.简单方便、重建效果更好, 能够适应各种场景;
    缺点 1.不能够得到深度信息, 重建效果较差;
    2.重建速度较慢.
    1.运算量大;
    2.基线距离较大时重建效果降低;
    3.价格较贵.
    1.设备结构复杂, 成本更高, 控制上难以实现;
    2.实时性较低, 易受光照的影响.
    下载: 导出CSV

    表  3  基于视觉的三维重建技术对比与分析

    Table  3  Comparison and analysis of 3D reconstruction based on vision

    方法 优点 缺点 自动化程度 重建效果 实时性 应用场景
    接触式方法[18] 快速直接测量物体的三维信息; 重建结果精度比较高 必须接触测量物体, 测量时物体表面容易被划伤 难以实现自动化重建 重建质量效果较好 实时 不能被广泛的应用, 只能应用到测量仪器能接触到的场景
    激光扫描法[28-31] 重建的模型很精确; 重建形状不规则物体的三维模型 形成的三维点云数据量非常庞大, 不容易处理; 重建的三维模型会产生漏洞; 设备比较复杂, 价格非常昂贵 一定程度的自动化重建 重建的三维模型很好 实时 目前主要应用在工厂的生产和检测中, 无法被广泛使用
    结构光法[32-42] 仅需要一幅图像就能获得物体形状; 简单方便; 无破坏性 重建速度较慢 一定程度的自动化重建 重建效果的精度比较高 实时 适用于室内场景
    阴影法[43-48] 设备简单低耗; 对图像的要求非常低 对光源有一定的要求 自动化重建较低 重建效果较差, 重建过程比较复杂 实时 无法被广泛使用
    TOF技术[49-53] 数据采集频率高; 垂直视场角大; 可以直接提取几何信息 深度测量系统误差大; 灰度图像对比度差、分辨率低; 搜索空间大、效率低; 算法扩展性差, 空间利用率低 一定程度的自动化重建 重建效果的精度较低 实时 能够广泛应用在人脸检测、车辆安全等方面
    雷达技术[54-58] 视场大、扫描距离远、灵敏度高、功耗低; 直接获取深度信息, 不用对内部参数进行标定 受环境的影响较大; 计算量较大, 实时性较差; 价格较贵 一定程度的自动化重建 重建效果一般 实时 能够广泛应用于各行各业
    Kinect技术[59-67] 价格便宜、轻便; 受光照条件的影响较小; 同时获取深度图像和彩色图像 深度图中含有大量的噪声; 对单张图像的重建效果较差 一定程度的自动化重建 重建效果较好 实时 能够被广泛应用于室内场景
    明暗度法[69-72] 重建结果比较精确应用范围广泛 易受光源影响; 依赖数学运算; 鲁棒性较差 完全自动化重建 在光源比较差的情况下重建效果较差 非实时 难以应用于镜面物体以及室外场景物体的三维重建
    光度立体视觉法[73-82] 避免了明暗度法存在的一些问题; 重建精度较高 易受光源影响; 鲁棒性较差 一定程度的自动化重建 重建效果较好 非实时 难以应用于镜面物体以及室外场景物体的三维重建
    纹理法[83-86] 对光照和噪声都不敏感; 重建精度较高 通用性较低; 速度较快; 鲁棒性较好 完全自动化重建 重建效果的精度较高 非实时 只适用于具有规则纹理的物体
    轮廓法[87-93] 重建效率非常高; 复杂度较低 对输入信息的要求很苛刻; 无法对物体表面的空洞和凹陷部分进行重建 完全自动化重建 重建效果取决于轮廓图像数量, 轮廓图像越多重建越精确 非实时 通常应用于对模型细节精度要求不是很高的三维重建中
    调焦法[94-96] 对光源条件要求比较宽松; 可使用少量图像测量物体表面信息 很难实现自动重建; 需要多张图片才能进行重建 不能实现自动化重建 重建效果比较好 非实时 对纹理复杂物体的重建效果较差, 不能广泛应用
    亮度法[97-100] 可全自动、无手工交互地进行高精度建模; 对光照条件要求宽松 鲁棒性较低; 灵活性较低; 复杂度较高 自动化重建 重建效果比较精细 非实时 可应用于文物数字化和人脸自动建模等领域
    单目视觉法[68] 简单方便、价格便宜、灵活可靠、使用范围广; 可以实现重建过程中的摄像机自标定, 处理时间短 不能够得到深度信息, 重建速度较慢 自动化重建 重建效果较差 实时 可应用于各种场景
    双目视觉法[101-110, 112] 方法成熟; 能够获得较好的重建效果 运算量大; 价格较贵; 在基线距离较大时重建效果降低 完全自动化重建 基线在一定条件下重建效果较好 实时 适用于室外场景, 应用范围广泛
    多目视觉法[111, 113-119] 识别精度高, 适应性较强, 视野范围大 运算量较大; 价格昂贵, 重建时间长 完全自动化重建 基线距离较大的情况下重建效果明显降低, 而且测量精度下降, 速度受限 实时 能够适应各种场景, 在很多范围内都可以使用
    区域视觉法[120-126] 计算简单; 匹配速度有所提高; 匹配精度较高; 提高了稠密匹配效率 受光线干扰较大; 对图像要求较高; 实验对象偏少 一定程度的自动化重建 重建结果较好 非实时 适用于各种领域, 例如, 视觉导航、遥感测绘
    特征视觉法[154-166] 提取简单; 抗干扰能力强; 鲁棒性好; 时间和空间复杂度低 不能够对图像信息进行全面的描述 完全自动化重建 能够较精确地对物体实现三维重建 实时 应用范围较广
    运动恢复结构法[127-139] 实用价值较高; 鲁棒性较强; 对图像的要求较低 计算量较大, 重建时间较长 完全自动化重建 重建效果取决于获取图像数量, 图像越多重建效果越好 实时 一般适用于大规模场景中
    因子分解法[145-148] 简便灵活, 抗噪能力强, 不依赖于其他模型 精度较低, 运算时间较长 完全自动化重建 重建效果精度较低 实时 一般适用于大场景中
    多视图几何法[149-163] 实用性较高; 通用性较强; 能够解决运动恢复结构法中的一些问题 计算量较大, 重建时间较长 一定程度完全自动化重建 重建效果比较好 实时 一般应用于静止的场景
    统计学习法[164-173] 重建质量和效率都很高; 基本不需要人工交互 获取的信息和数据库目标不一致时, 重建结果与目标相差甚远 一定程度的自动化重建 重建效果取决于数据库的完整程度, 数据库越完备重建效果越好 非实时 适用于大场景、识别和视频检索系统
    神经网络法[174-177] 精度较高, 具有很强的鲁棒性 收敛速度慢, 运算量较大 一定程度完全自动化重建 重建效果较好 实时 能够应用于各种领域, 例如计算机视觉、军事及航天等
    深度学习与语义法[178-181] 计算简单, 精度较高, 不需要进行复杂的几何运算, 实时性较好 训练时间较长, 对CPU的要求较高 一定程度完全自动化重建 重建结果取决于训练的好坏 实时 适用于各种大规模场景
    下载: 导出CSV
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