田茂,花向红

• 1:重庆邮电大学计算机科学与技术学院
• 2:武汉大学测绘学院

摘要(Abstract)：

针对传统立体匹配算法视差图重建效率低、鲁棒性差的问题,该文提出一种基于树动态规划的超像素层次立体匹配方法。该方法将视差图重建问题转化为基于倾斜平面的连续全局能量最优化模型,利用PatchMatch和树动态规划策略实施基于超像素层次的能量模型最优化,并通过实验数据对本文算法的有效性和鲁棒性进行验证。实验结果表明,该算法能够快速、高精度地重建三维场景几何结构。

关键词(KeyWords)： 立体匹配;树动态规划;能量最优化模型;超像素结构

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学青年科学基金项目(42001417)

作者(Author): 田茂,花向红

DOI: 10.16251/j.cnki.1009-2307.2021.12.017

参考文献(References)：

• [1]HIRSCHMULLER H.Stereo processing by semiglobal matching and mutual information[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(2):328-341．
• [2]DRYANOVSKI I,KLINGENSMITH M,SRINIVASAS S,et al.Large-scale,real-time 3Dscene reconstruction on a mobile device[J].Autonomous Robots,2017,41(6):1423-1445．
• [3]朱庆，陈崇泰，胡翰，等．顾及纹理特征的航空影像自适应密集匹配方法[J]．测绘学报，2017,46(1):62-72.(ZHU Qing,CHEN Chongtai,HU Han,et al.An adaptive dense matching method for airborne images using texture information[J].Acta Geodaetica et Cartographica Sinica,2017,46(1):62-72．）
• [4]FURUKAWA Y,HERNáNDEZ C.Multi-view stereo:a tutorial[J].Foundations and Trends? in Computer Graphics and Vision,2015,9(1/2):1-148．
• [5]YAN Li,FEI Liang,CHEN Changhai,et al.A multiview dense image matching method for high-resolution aerial imagery based on a graph network[J].Remote Sensing,2016,8(10):799．
• [6]CHEN C Y,SEFF A,KORNHAUSER A,et al.DeepDriving:learning affordance for direct perception in autonomous driving[C]//2015IEEE International Conference on Computer Vision(ICCV).Santiago,Chile:IEEE,2015:2722-2730．
• [7]HOU Yaolin,PENG Jianwei,HU Zhihua,et al.Planarity constrained multi-view depth map reconstruction for urban scenes[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,139:133-145．
• [8]HU Pingbo,YANG Bisheng,DONG Zhen,et al.Towards reconstructing 3D buildings from ALS data based on gestalt laws[J].Remote Sensing,2018,10(7):1127．
• [9]SCHARSTEIN D,SZELISKI R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J].International Journal of Computer Vision,2002,47(1/2/3):7-42．
• [10]LI Y,MIN D B,BROWN M S,et al.SPM-BP:Sped-up PatchMatch belief propagation for continuous MRFs[C]//2015IEEE International Conference on Computer Vision(ICCV).Santiago,Chile:IEEE,2015:4006-4014．
• [11]TANIAI T,MATSUSHITA Y,SATO Y,et al.Continuous 3Dlabel stereo matching using local expansion moves[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(11):2725-2739．
• [12]YOON K J,KWEON I S.Adaptive support-weight approach for correspondence search[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656．
• [13]BLEYER M,RHEMANN C,ROTHER C.PatchMatch stereo-stereo matching with slanted support windows[C]//Procedings of the British Machine Vision Conference2011.Dundee,British:Machine Vision Association,2011．
• [14]YANG Qingxiong.A non-local cost aggregation method for stereo matching[C]//2012IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI,USA:IEEE,2012:1402-1409．
• [15]LI L,YU X,ZHANG S,et al.3Dcost aggregation with multiple minimum spanning trees for stereo matching[J].Applied Optics,2017,56(12):3411-3420．
• [16]BOYKOV Y,VEKSLER O,ZABIH R.Fast approximate energy minimization via graph cuts[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(11):1222-1239．
• [17]VEKSLER O.Stereo correspondence by dynamic programming on a tree[C]//2005IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'05).San Diego,CA,USA:IEEE,2005:384-390．
• [18]SUN Jian,ZHENG Nanning,SHUM H Y.Stereo matching using belief propagation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(7):787-800．
• [19]KLAUS A,SORMANN M,KARNER K.Segmentbased stereo matching using belief propagation and a self-adapting dissimilarity measure[C]//18th International Conference on Pattern Recognition (ICPR'06).Hong Kong,China:IEEE,2006:15-18．
• [20]YAMAGUCHI K,MCALLESTER D,URTASUN R.Efficient joint segmentation,occlusion labeling,stereo and flow estimation[C]//Computer Vision-ECCV 2014,2014．
• [21]GüNEY F,GEIGER A.Displets:resolving stereo ambiguities using object knowledge[C]//2015IEEEConference on Computer Vision and Pattern Recognition(CVPR).Boston,MA,USA:IEEE,2015:4165-4175．
• [22]HADFIELD S,LEBEDA K,BOWDEN R.Stereo reconstruction using top-down cues[J].Computer Vision and Image Understanding,2017,157:206-222．
• [23]HOSNI A,BLEYER M,GELAUTZ M.Secrets of adaptive support weight techniques for local stereo matching[J].Computer Vision and Image Understanding,2013,117(6):620-632．
• [24]BARNES C,SHECHTMAN E,FINKELSTEIN A,et al.PatchMatch[J].ACM Transactions on Graphics,2009,28(3):1-11．
• [25]BESSE F,ROTHER C,FITZGIBBON A,et al.PMBP:PatchMatch belief propagation for correspondence field estimation[J].International Journal of Computer Vision,2014,110(1):2-13．
• [26]HE Kaiming,SUN Jian,TANG Xiaoou.Guided image filtering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409．
• [27]LI Lincheng,ZHANG Shunli,YU Xin,et al.PMSC:PatchMatch-based superpixel cut for accurate stereo matching[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,28(3):679-692．
• [28]LU Jiangbo,LI Yu,YANG Hongsheng,et al.PatchMatch filter:edge-aware filtering meets randomized search for visual correspondence[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(9):1866-1879．
• [29]HIRSCHMULLER H,SCHARSTEIN D.Evaluation of stereo matching costs on images with radiometric differences[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(9):1582-1599．
• [30]FELZENSZWALB P F,HUTTENLOCHER D P.Efficient graph-based image segmentation[J].International Journal of Computer Vision,2004,59(2):167-181．
• [31]MENZE M,GEIGER A.Object scene flow for autonomous vehicles[C]//2015IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Boston,MA,USA:IEEE,2015:3061-3070．
• [32]GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving?The KITTI vision benchmark suite[C]//2012IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI,USA:IEEE,2012:3354-3361.