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快速鲁棒的城市场景分段平面重建

王伟 高伟 朱海 胡占义

王伟, 高伟, 朱海, 胡占义. 快速鲁棒的城市场景分段平面重建. 自动化学报, 2017, 43(4): 674-684. doi: 10.16383/j.aas.2017.c160261
引用本文: 王伟, 高伟, 朱海, 胡占义. 快速鲁棒的城市场景分段平面重建. 自动化学报, 2017, 43(4): 674-684. doi: 10.16383/j.aas.2017.c160261
WANG Wei, GAO Wei, ZHU Hai, HU Zhan-Yi. Rapid and Robust Piecewise Planar Reconstruction of Urban Scenes. ACTA AUTOMATICA SINICA, 2017, 43(4): 674-684. doi: 10.16383/j.aas.2017.c160261
Citation: WANG Wei, GAO Wei, ZHU Hai, HU Zhan-Yi. Rapid and Robust Piecewise Planar Reconstruction of Urban Scenes. ACTA AUTOMATICA SINICA, 2017, 43(4): 674-684. doi: 10.16383/j.aas.2017.c160261

快速鲁棒的城市场景分段平面重建

doi: 10.16383/j.aas.2017.c160261
基金项目: 

河南省高校重点科研项目 17A520018

国家自然科学基金 61472419

河南省科技攻关项目 162102310589

河南省高校重点科研项目 15A520116

河南省高校重点科研项目 16B520034

河南省自然科学基金 162300410347

河南省科技攻关项目 162102210396

河南省高校重点科研项目 17A520019

国家高技术研究发展计划(863计划) 2015AA124102

国家自然科学基金 61273280

周口师范学院高层次人才科研启动基金 zknuc2015103

国家自然科学基金 61333015

详细信息
    作者简介:

    王伟 周口师范学院网络工程学院副教授.2014年获得中国科学院自动化研究所博士学位.主要研究方向为计算机视觉和模式识别.E-mail:wangwei@zknu.cn

    朱海 周口师范学院网络工程学院副教授.2010年获得西安电子科技大学博士学位.主要研究方向为计算机视觉和云计算.E-mail:zhusea@163.com

    胡占义 中国科学院自动化研究所研究员.1993年获得比利时列日大学博士学位.主要研究方向为计算机视觉和机器学习.E-mail:huzy@nlpr.ia.ac.cn

    通讯作者:

    高伟 中国科学院自动化研究所副研究员.2008年获得中国科学院自动化研究所博士学位.主要研究方向为计算机视觉和三维重建.E-mail:wgao@nlpr.ia.ac.cn

Rapid and Robust Piecewise Planar Reconstruction of Urban Scenes

Funds: 

College Key Research Project of Henan 17A520018

National Natural Science Foundation of China 61472419

Key Scientiflc and Technological Project of Henan 162102310589

College Key Research Project of Henan 15A520116

College Key Research Project of Henan 16B520034

Natural Science Foundation of of Henan Province 162300410347

Key Scientiflc and Technological Project of Henan 162102210396

College Key Research Project of Henan 17A520019

National High Technology Research and Development Program of China (863 Program) 2015AA124102

National Natural Science Foundation of China 61273280

Scientiflc Research Starting Foundation for Advanced Talents of Zhoukou Normal University zknuc2015103

National Natural Science Foundation of China 61333015

More Information
    Author Bio:

    Associate professor at the School of Network Engineering, Zhoukou Normal University. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2014. His research interest covers computer vision and pattern recognition

    Associate professor at the School of Network Engineering, Zhoukou Normal University. He received his Ph. D. degree from Xidian University in 2010. His research interest covers computer vision and cloud computing

    Professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from the University of Liege, Belgium in 1993. His research interest covers computer vision and machine learning

    Corresponding author: GAO Wei Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2008. His research interest covers computer vision and 3D reconstruction. Corresponding author of this paper
  • 摘要: 对于基于图像的城市场景重建,由于光照变化、透视畸变、弱纹理区域等因素的影响,传统像素级与区域级的重建算法通常难以获得可靠的重建结果.为了解决此问题,本文提出一种快速、鲁棒的分段平面重建算法.根据城市场景结构特征与分段平面假设,本文算法首先利用基于连通域检测的空间平面拟合方法从初始空间点中抽取充分且可靠的候选空间平面,然后在MRF(Markov random field)能量最小化框架下将场景的完整结构推断问题转化为平面标记问题进行求解.由于候选平面集与融合灰度一致性度量、空间几何与可见性约束的能量模型的高可靠性,场景的完整结构因此可被有效地重建.实验结果表明,本文算法能较好地克服传统算法可靠性差、重建场景不完整等缺点,同时具有较高的计算效率.
    1)  本文责任编委 贾云得
  • 图  1  摄像机与图像示例

    Fig.  1  Cameras and sample images

    图  2  城市场景重建问题

    Fig.  2  Problems in reconstructing urban scenes

    图  3  本文算法基本流程图

    Fig.  3  Flowchart of our proposed method

    图  4  候选平面的抽取

    Fig.  4  Extraction of candidate planes

    图  5  场景结构的推断

    Fig.  5  Inference of scene structures

    图  6  数据集例图 (上:标准数据; 下:真实数据)

    Fig.  6  Sample images (Up: standard data; Down: real-world data)

    图  7  标准数据集实验结果

    Fig.  7  Results for standard data sets

    图  8  真实数据集实验结果 (图像从上而下为: #1, #2, #3和#4)

    Fig.  8  Results for real-world data sets (Images from top to bottom are #1, #2, #3 and #4.)

    图  9  多幅图像对应的初始空间点与本文算法重建结果(左:场景#1;右:场景#2)

    Fig.  9  Initial 3D points and reconstruction results of multiple images (Left: scene #1; Right: scene #2)

    表  1  在标准数据集上的算法性能比较

    Table  1  Performance comparisons on standard data sets

    图像 初始空间点数量 本文算法 文献[12]
    超像素数量 平面数量 重建精度 时间 (s) 超像素数量 平面数量 重建精度 时间 (s)
    抽取 推断 合计 抽取 推断 合计
    Herz-Jesu 29 541 49 326 12 0.8043 73.15 138.81 211.96 3 096 7 0.3296 136.72 124.50 258.22
    Castle 12 337 26 825 9 0.7452 57.96 218.06 276.01 4 127 5 0.2701 107.40 196.80 304.21
    下载: 导出CSV

    表  2  在真实数据集上的算法性能比较

    Table  2  Performance comparisons on real-world data sets

    图像序号 初始空间点 本文算法 文献[12]
    超像素 平面 精度 时间 (s) 超像素 平面 精度 时间 (s)
    #1 16 520 13 907 6 0.7554 58.22 1 996 4 0.3187 78.13
    #2 20 468 9 562 7 0.7852 49.36 1 768 6 0.4165 60.98
    #3 13 710 23 955 6 0.6486 70.01 2 341 6 0.3547 76.39
    #4 10 694 18 933 7 0.5713 64.52 3 011 5 0.2310 72.67
    下载: 导出CSV
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    Wang Wei, Gao Wei, Hu Zhan-Yi. Dense 3D scene reconstruction based on semantic constraint and graph cuts. Science China Information Sciences, 2014, 44(6):774-792 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201406009.htm
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出版历程
  • 收稿日期:  2016-03-11
  • 录用日期:  2016-07-11
  • 刊出日期:  2017-04-20

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