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一种基于场景图分割的混合式多视图三维重建方法

薛俊诗 易辉 吴止锾 陈向宁

薛俊诗, 易辉, 吴止锾, 陈向宁. 一种基于场景图分割的混合式多视图三维重建方法. 自动化学报, 2020, 46(4): 782-795. doi: 10.16383/j.aas.c180155
引用本文: 薛俊诗, 易辉, 吴止锾, 陈向宁. 一种基于场景图分割的混合式多视图三维重建方法. 自动化学报, 2020, 46(4): 782-795. doi: 10.16383/j.aas.c180155
XUE Jun-Shi, YI Hui, WU Zhi-Huan, CHEN Xiang-Ning. A Hybrid Multi-View 3D Reconstruction Method Based on Scene Graph Partition. ACTA AUTOMATICA SINICA, 2020, 46(4): 782-795. doi: 10.16383/j.aas.c180155
Citation: XUE Jun-Shi, YI Hui, WU Zhi-Huan, CHEN Xiang-Ning. A Hybrid Multi-View 3D Reconstruction Method Based on Scene Graph Partition. ACTA AUTOMATICA SINICA, 2020, 46(4): 782-795. doi: 10.16383/j.aas.c180155

一种基于场景图分割的混合式多视图三维重建方法

doi: 10.16383/j.aas.c180155
基金项目: 

国家高技术研究发展计划(863)计划 2014AA7031072E

军队探索项目 7131145

详细信息
    作者简介:

    易辉  航天工程大学航天信息学院博士研究生.主要研究方向为计算机视觉, 摄影测量与遥感. E-mail:18810962910@163.com

    吴止锾  航天工程大学航天信息学院博士研究生.主要研究方向为遥感图像处理. E-mail: wuzhihuan@hotmail.com

    陈向宁  航天工程大学航天信息学院教授.主要研究方向为计算机视觉, 图像处理, 机器学习. E-mail:laser115@163.com

    通讯作者:

    薛俊诗  航天工程大学航天信息学院博士研究生.主要研究方向为计算机视觉, 摄影测量与遥感.本文通信作者.E-mail: xueao2015@sina.com

  • 本文责任编委 吴毅红

A Hybrid Multi-View 3D Reconstruction Method Based on Scene Graph Partition

Funds: 

National High Technology Research and Development Program of China (863 Program) 2014AA7031072E

Exploration Project of the Army 7131145

More Information
    Author Bio:

    YI Hui   Ph. D. candidate at the School of Space Information, Space Engineering University. His research interest covers computer vision, photogrammetry, and remote sensing

    WU Zhi-Huan  Ph. D. candidate at the School of Space Information, Space Engineering University. His main research interest is remote sensing image processing

    CHEN Xiang-Ning   Professor at the School of Space Information, Space Engineering University. His research interest covers computer vision, image processing, and machine learning

    Corresponding author: XUE Jun-Shi   Ph. D. candidate at the School of Space Information, Space Engineering University. His research interest covers computer vision, photogrammetry, and remote sensing. Corresponding author of this paper
  • Recommended by Associate Editor WU Yi-Hong
  • 摘要: 针对大范围三维重建, 重建效率较低和重建稳定性、精度差等问题, 提出了一种基于场景图分割的大范围混合式多视图三维重建方法.该方法首先使用多层次加权核K均值算法进行场景图分割; 然后,分别对每个子场景图进行混合式重建, 生成对应的子模型, 通过场景图分割、混合式重建和局部优化等方法提高重建效率、降低计算资源消耗, 并综合采用强化的最佳影像选择标准、稳健的三角测量方法和迭代优化等策略, 提高重建精度和稳健性; 最后, 对所有子模型进行合并, 完成大范围三维重建.分别使用互联网收集数据和无人机航拍数据进行了验证, 并与1DSFM、HSFM算法在计算精度和计算效率等方面进行了比较.实验结果表明, 本文算法大大提高了计算效率、计算精度, 能充分保证重建模型的完整性, 并具备单机大范围场景三维重建能力.
    Recommended by Associate Editor WU Yi-Hong
    1)  本文责任编委 吴毅红
  • 图  1  基于场景分割的三维重建流程图

    Fig.  1  3D reconstruction based on scene partition pipeline

    图  2  多层次场景分割示意图

    Fig.  2  Diagram of multi-level scene segmentation partition

    图  3  子场景分割示意图

    Fig.  3  Diagram of Sub-scene Relationship

    图  4  子场景图混合式重建流程图

    Fig.  4  Hybrid reconstruction of sub-scene graph pipeline

    图  5  新增影像重建点分布情况示意图

    Fig.  5  Distribution of the points in selected image

    图  6  重建结果中的影像连接关系

    Fig.  6  The image connecting relationship of 3D reconstruction result

    图  7  大雁塔场景图分割重建结果(上:分割后子场景图; 中:子场景图相机地面投影; 下:子场景图重建结果)

    Fig.  7  Reconstruction results of DAYANTA based on scene graph partition (up: sub-scene graph; middle: camera ground projection in sub-scene graph; below: reconstruction result of sub-scene graph)

    图  8  场景图分割重建结果(上:分割后子场景图; 下:子场景图重建结果)

    Fig.  8  Reconstruction results of Rome Forum based on scene graph partition (up: sub-scene graph; below: reconstruction result of sub-scene graph)

    图  9  互联网数据集三维重建结果

    Fig.  9  3D reconstruction result of datasets downloaded from internet

    图  10  航测数据集(西安大雁塔、大连市区、某城市市区)三维重建结果

    Fig.  10  3D reconstruction result of aerial images (Da-Yan Tower in Xi$'$an, Dalian City, and A City Center)

    图  11  稀疏重建结果(左:嵩山, 右: Quad)

    Fig.  11  Sparse reconstruction results (left: Songshan; right: Quad)

    图  12  嵩山地区部分区域特征点与重投影点分布图(×表示特征点位置, +表示重投影点位置)

    Fig.  12  Distribution of feature points and re-projection points in the Songshan areas (×: feature points, +: re-projection points)

    表  1  合并前后重建结果对比

    Table  1  Comparison of reconstruction results before and after merging

    数据集 子场景序号 合并前 合并后
    $N_R$ $A_{RE}$ $N_R$ $A_{RE}$
    大雁塔 1 296 0.416 1 045 0.455
    2 323 0.463
    3 268 0.451
    4 253 0.437
    Rome Forum 1 470 0.577 1 475 0.572
    2 386 0.556
    3 355 0.583
    4 431 0.572
    表中, $N_R$为重建影像数目; $A_{RE}$表示重投影误差, 单位(pixel).
    下载: 导出CSV

    表  2  1DSFM、HSFM与本文算法的不同数据集三维重建结果对比

    Table  2  Comparison of 3D reconstruction result of different dataset using 1DSFM, HSFM and Ours

    数据集 1DSFM HSFM Ours
    名称 $N_D$ $N_R$ $T_{A}$ $A_{RE}$ $N_R$ $T_{A}$ $A_{RE}$ $N_R$ $T_{A}$ $A_{RE}$
    龙泉寺 443 406 25.386 0.841 417 19.661 0.717 413 16.815 0.711
    Yorkminster 3 368 1 176 93.910 0.736 1 472 64.726 0.628 1 712 65.803 0.607
    Piccadilly 7 351 6 445 476.781 1.194 6 791 269.561 0.865 6 979 231.794 0.822
    Trafalgar 15 683 9 384 765.685 1.173 11 943 481.857 0.837 12 741 438.443 0.816
    西安大雁塔 1 045 1 043 58.357 0.617 1 045 31.637 0.510 1 045 26.946 0.455
    大连市区 4 900 4 900 327.625 1.397 4 900 231.394 1.478 4 900 197.872 1.353
    某城市市区 15 750 NA NA NA 15 745 845.632 1.359 15 750 785.451 1.211
    表中, $N_D$为数据集中的影像数目; $N_R$为重建影像数目; $T_{A}$表示重建时间, 单位(min); $A_{RE}$表示重投影误差, 单位(pixel).
    下载: 导出CSV

    表  3  嵩山地区部分误差结果

    Table  3  Partial error result of the Songshan area

    序号 $\Delta X$ $\Delta Y$ $\Delta Z$ RMS
    1 0.2727 0.2337 0.2512 0.4383
    2 0.1222 0.1053 0.1597 0.3370
    3 0.1725 0.3045 0.3623 0.5037
    4 0.2045 0.3643 0.5239 0.6703
    5 0.1380 0.1419 0.4578 0.4987
    下载: 导出CSV

    表  4  Quad数据集部分误差结果

    Table  4  Partial error result of Quad dataset

    序号 $\Delta X$ $\Delta Y$ $\Delta Z$ RMS
    1 0.2727 0.2337 0.2512 0.4383
    2 0.1222 0.1053 0.1597 0.3370
    3 0.1725 0.3045 0.3623 0.5037
    4 0.2045 0.3643 0.5239 0.6703
    5 0.1380 0.1419 0.4578 0.4987
    下载: 导出CSV
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