光学学报, 2020, 40 (2): 0215001, 网络出版: 2020-01-02   

融合多尺度局部特征与深度特征的双目立体匹配 下载: 1636次

Binocular Stereo Matching by Combining Multiscale Local and Deep Features
作者单位
1 重庆大学光电技术及系统教育部重点实验室, 重庆 400040
2 重庆大学光电工程学院, 重庆 400040
3 重庆师范大学计算机与信息科学学院, 重庆 401331
引用该论文

王旭初, 刘辉煌, 牛彦敏. 融合多尺度局部特征与深度特征的双目立体匹配[J]. 光学学报, 2020, 40(2): 0215001.

Xuchu Wang, Huihuang Liu, Yanmin Niu. Binocular Stereo Matching by Combining Multiscale Local and Deep Features[J]. Acta Optica Sinica, 2020, 40(2): 0215001.

参考文献

[1] 肖进胜, 田红, 邹文涛, 等. 基于深度卷积神经网络的双目立体视觉匹配算法[J]. 光学学报, 2018, 38(8): 0815017.

    Xiao J S, Tian H, Zou W T, et al. Stereo matching based on convolutional neural network[J]. Acta Optica Sinica, 2018, 38(8): 0815017.

[2] GeigerA, RoserM, UrtasunR. Efficient large-scale stereo matching[M] ∥Kimmel R, Klette R, Sugimoto A. Computer vision-ACCV 2010. Lecture notes in computer science. Berlin, Heidelberg: Springer, 2011, 6492: 25- 38.

[3] WangP, WuF. A local stereo matching algorithm based on region growing[M] ∥Zhang W, Yang X, Xu Z, et al. Advances on digital television and wireless multimedia communications. Communications in computer and information science. Berlin, Heidelberg: Springer, 2012, 331: 459- 464.

[4] Chang XF, ZhouZ, WangL, et al. Real-time accurate stereo matching using modified two-pass aggregation and winner-take-all guided dynamic programming[C]∥2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, May 16-19, 2011, Hangzhou, China. New York: IEEE, 2011: 73- 79.

[5] 马宁, 门宇博, 门朝光, 等. 基于扩展相位相关的小基高比立体匹配方法[J]. 电子学报, 2017, 45(8): 1827-1835.

    Ma N, Men Y B, Men C G, et al. A small baseline stereo matching method based on extended phase correlation[J]. Acta Electronica Sinica, 2017, 45(8): 1827-1835.

[6] Yang YY, Wang HB, LiuB. A new stereo matching algorithm based on adaptive window[C]∥2012 International Conference on Systems and Informatics (ICSAI2012), May 19-20, 2012, Yantai, China. New York: IEEE, 2012: 1815- 1819.

[7] 马瑞浩, 朱枫, 吴清潇, 等. 基于图像分割的稠密立体匹配算法[J]. 光学学报, 2019, 39(3): 0315001.

    Ma R H, Zhu F, Wu Q X, et al. Dense stereo matching algorithm based on image segmentation[J]. Acta Optica Sinica, 2019, 39(3): 0315001.

[8] 刘艳, 李庆武, 霍冠英, 等. 结合局部二进制表示和超像素分割求精的立体匹配[J]. 光学学报, 2018, 38(6): 0615003.

    Liu Y, Li Q W, Huo G Y, et al. Local binary description combined with superpixel segmentation refinement for stereo matching[J]. Acta Optica Sinica, 2018, 38(6): 0615003.

[9] 李培玄, 刘鹏飞, 曹飞道, 等. 自适应权值的跨尺度立体匹配算法[J]. 光学学报, 2018, 38(12): 1215006.

    Li P X, Liu P F, Cao F D, et al. Weight-adaptive cross-scale algorithm for stereo matching[J]. Acta Optica Sinica, 2018, 38(12): 1215006.

[10] Chen D M, Ardabilian M, Chen L M. A fast trilateral filter-based adaptive support weight method for stereo matching[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(5): 730-743.

[11] ChenJ, Cai CH, Li CH. A multi-window stereo matching algorithm in rank tranform domain[C]∥2012 IEEE 11th International Conference on Signal Processing, October 21-25, 2012, Beijing, China. New York: IEEE, 2012: 997- 1000.

[12] HirschmullerH. Accurate and efficient stereo processing by semi-global matching and mutual information[C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 2005: 8624110.

[13] ŽbontarJ, LeCun Y. Computing the stereo matching cost with a convolutional neural network[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 1592- 1599.

[14] Žbontar J. LeCun Y. Stereo matching by training a convolutional neural network to compare image patches[J]. Journal of Machine Learning Research, 2016, 17: 1-32.

[15] ZagoruykoS, KomodakisN. Learning to compare image patches via convolutional neural networks[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 4353- 4361.

[16] Chen ZY, SunX, WangL, et al. A deep visual correspondence embedding model for stereo matching costs[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 972- 980.

[17] Zhang K, Lu J B, Lafruit G. Cross-based local stereo matching using orthogonal integral images[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19(7): 1073-1079.

[18] Hirschmüller H. Stereo processing by semiglobal matching and mutual information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 328-341.

[19] Pal C J, Weinman J J, Tran L C, et al. On learning conditional random fields for stereo[J]. International Journal of Computer Vision, 2012, 99(3): 319-337.

[20] KnobelreiterP, ReinbacherC, ShekhovtsovA, et al. End-to-end training of hybrid CNN-CRF models for stereo[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 1456- 1465.

[21] SekiA, PollefeysM. Patch based confidence prediction for dense disparity map[C]∥Procedings of the British Machine Vision Conference 2016, September 19-22, 2016, York, UK.UK: BMVA Press, 2016: 23.

[22] PoggiM, MattocciaS. Learning to predict stereo reliability enforcing local consistency of confidence maps[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 4541- 4550.

[23] GüneyF, GeigerA. Displets: resolving stereo ambiguities using object knowledge[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 4165- 4175.

[24] KendallA, MartirosyanH, DasguptaS, et al. End-to-end learning of geometry and context for deep stereo regression[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 66- 75.

[25] Brandao P, Mazomenos E, Stoyanov D. Widening siamese architectures for stereo matching[J]. Pattern Recognition Letters, 2019, 120: 75-81.

[26] Park H, Lee K M. Look wider to match image patches with convolutional neural networks[J]. IEEE Signal Processing Letters, 2017, 24(12): 1788-1792.

[27] LencK, VedaldiA. Understanding image representations by measuring their equivariance and equivalence[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 991- 999.

[28] Walia E, Verma V. Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval[J]. International Journal of Multimedia Information Retrieval, 2016, 5(3): 173-184.

[29] Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

[30] GeigerA, LenzP, UrtasunR. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 2012: 3354- 3361.

[31] MenzeM, GeigerA. Object scene flow for autonomous vehicles[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 3061- 3070.

王旭初, 刘辉煌, 牛彦敏. 融合多尺度局部特征与深度特征的双目立体匹配[J]. 光学学报, 2020, 40(2): 0215001. Xuchu Wang, Huihuang Liu, Yanmin Niu. Binocular Stereo Matching by Combining Multiscale Local and Deep Features[J]. Acta Optica Sinica, 2020, 40(2): 0215001.

本文已被 6 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!