{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:23:50Z","timestamp":1773771830807,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s11432-019-2803-x","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T04:09:48Z","timestamp":1597205388000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["InStereo2K: a large real dataset for stereo matching in indoor scenes"],"prefix":"10.1007","volume":"63","author":[{"given":"Wei","family":"Bao","sequence":"first","affiliation":[]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuhua","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yulan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Siyu","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Xiaohu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,31]]},"reference":[{"key":"2803_CR1","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), 2012","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2803_CR2","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A. Object scene flow for autonomous vehicles. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"2803_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patrec.2019.08.016","volume":"128","author":"D Li","year":"2019","unstructured":"Li D, Liu N, Guo Y, et al. pose estimation for random bin-picking using partition viewpoint feature histograms. Pattern Recogn Lett, 2019, 128: 148\u2013154","journal-title":"Pattern Recogn Lett"},{"key":"2803_CR4","doi-asserted-by":"crossref","unstructured":"Khan S H, Guo Y, Hayat M, et al. Unsupervised primitive discovery for improved 3D generative modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 9739\u20139748","DOI":"10.1109\/CVPR.2019.00997"},{"key":"2803_CR5","doi-asserted-by":"publisher","first-page":"029102","DOI":"10.1007\/s11432-017-9473-5","volume":"62","author":"W Wang","year":"2019","unstructured":"Wang W, Gao W, Hu Z Y. Effectively modeling piecewise planar urban scenes based on structure priors and CNN. Sci China Inf Sci, 2019, 62: 029102","journal-title":"Sci China Inf Sci"},{"key":"2803_CR6","doi-asserted-by":"publisher","first-page":"3885","DOI":"10.1109\/TIP.2019.2903318","volume":"28","author":"T Yan","year":"2019","unstructured":"Yan T, Gan Y, Xia Z, et al. Segment-based disparity refinement with occlusion handling for stereo matching. IEEE Trans Image Process, 2019, 28: 3885\u20133897","journal-title":"IEEE Trans Image Process"},{"key":"2803_CR7","doi-asserted-by":"publisher","unstructured":"Liang Z, Guo Y, Feng Y, et al. Stereo matching using multi-level cost volume and multi-scale feature constancy. IEEE Trans Pattern Anal Mach Intell, 2019. doi: https:\/\/doi.org\/10.1109\/TPAMI.2019.2928550","DOI":"10.1109\/TPAMI.2019.2928550"},{"key":"2803_CR8","doi-asserted-by":"crossref","unstructured":"Khamis S, Fanello S, Rhemann C, et al. StereoNET: guided hierarchical refinement for real-time edge-aware depth prediction. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018. 573\u2013590","DOI":"10.1007\/978-3-030-01267-0_35"},{"key":"2803_CR9","doi-asserted-by":"crossref","unstructured":"Chang J R, Chen Y S. Pyramid stereo matching network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 5410\u20135418","DOI":"10.1109\/CVPR.2018.00567"},{"key":"2803_CR10","first-page":"31","volume-title":"High-resolution stereo datasets with subpixel-accurate ground truth","author":"D Scharstein","year":"2014","unstructured":"Scharstein D, Hirschm\u00fcller H, Kitajima Y, et al. High-resolution stereo datasets with subpixel-accurate ground truth. In: Prcoeedings of German Conference on Pattern Recognition. Berlin: Springer, 2014. 31\u201342"},{"key":"2803_CR11","unstructured":"Scharstein D, Szeliski R. High-accuracy stereo depth maps using structured light. In: Prcoeedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003"},{"key":"2803_CR12","doi-asserted-by":"crossref","unstructured":"Sch\u00fcops T, Sch\u00fconberger J L, Galliani S, et al. A multi-view stereo benchmark with high-resolution images and multicamera videos. In: Prcoeedings of Conference on Computer Vision and Pattern Recognition (CVPR), 2017","DOI":"10.1109\/CVPR.2017.272"},{"key":"2803_CR13","doi-asserted-by":"crossref","unstructured":"Mayer N, Ilg E, Hausser P, et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 4040\u20134048","DOI":"10.1109\/CVPR.2016.438"},{"key":"2803_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2002","unstructured":"Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vision, 2002, 47: 7\u201342","journal-title":"Int J Comput Vision"},{"key":"2803_CR15","first-page":"2","volume":"17","author":"J Zbontar","year":"2016","unstructured":"Zbontar J, LeCun Y. Stereo matching by training a convolutional neural network to compare image patches. J Mach Learn Res, 2016, 17: 2","journal-title":"J Mach Learn Res"},{"key":"2803_CR16","doi-asserted-by":"crossref","unstructured":"Mei X, Sun X, Zhou M, et al. On building an accurate stereo matching system on graphics hardware. In: Prcoeedings of IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011. 467\u2013474","DOI":"10.1109\/ICCVW.2011.6130280"},{"key":"2803_CR17","doi-asserted-by":"crossref","unstructured":"Luo W, Schwing A G, Urtasun R. Efficient deep learning for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 5695\u20135703","DOI":"10.1109\/CVPR.2016.614"},{"key":"2803_CR18","doi-asserted-by":"crossref","unstructured":"Shaked A, Wolf L. Improved stereo matching with constant highway networks and reflective confidence learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 4641\u20134650","DOI":"10.1109\/CVPR.2017.730"},{"key":"2803_CR19","doi-asserted-by":"crossref","unstructured":"Kendall A, Martirosyan H, Dasgupta S, et al. End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 66\u201375","DOI":"10.1109\/ICCV.2017.17"},{"key":"2803_CR20","doi-asserted-by":"crossref","unstructured":"Liang Z, Feng Y, Guo Y, et al. Learning for disparity estimation through feature constancy. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 2811\u20132820","DOI":"10.1109\/CVPR.2018.00297"},{"key":"2803_CR21","first-page":"483","volume-title":"Stacked hourglass networks for human pose estimation","author":"A Newell","year":"2016","unstructured":"Newell A, Yang K, Deng J. Stacked hourglass networks for human pose estimation. In: Prcoeedings of European Conference on Computer Vision. Berlin: Springer, 2016. 483\u2013499"},{"key":"2803_CR22","doi-asserted-by":"crossref","unstructured":"Zhang F, Prisacariu V, Yang R, et al. GA-Net: guided aggregation net for end-to-end stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019","DOI":"10.1109\/CVPR.2019.00027"},{"key":"2803_CR23","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1364\/OE.22.001287","volume":"22","author":"W Lohry","year":"2014","unstructured":"Lohry W, Chen V, Zhang S. Absolute three-dimensional shape measurement using coded fringe patterns without phase unwrapping or projector calibration. Opt Express, 2014, 22: 1287\u20131301","journal-title":"Opt Express"},{"key":"2803_CR24","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1364\/AO.46.000036","volume":"46","author":"S Zhang","year":"2007","unstructured":"Zhang S, Yau S T. Generic nonsinusoidal phase error correction for three-dimensional shape measurement using a digital video projector. Appl Opt, 2007, 46: 36\u201343","journal-title":"Appl Opt"},{"key":"2803_CR25","doi-asserted-by":"crossref","unstructured":"Scharstein D, Pal C. Learning conditional random fields for stereo. In: Prcoeedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007. 1\u20138","DOI":"10.1109\/CVPR.2007.383191"},{"key":"2803_CR26","doi-asserted-by":"crossref","unstructured":"Butler D J, Wulff J, Stanley G B, et al. A naturalistic open source movie for optical flow evaluation. In: Prcoeedings of European Conference on Computer Vision. Berlin: Springer, 2012. 611\u2013625","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"2803_CR27","doi-asserted-by":"crossref","unstructured":"Ros G, Sellart L, Materzynska J, et al. The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 3234\u20133243","DOI":"10.1109\/CVPR.2016.352"},{"key":"2803_CR28","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2803_CR29","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1007\/s11263-018-1082-6","volume":"126","author":"N Mayer","year":"2018","unstructured":"Mayer N, Ilg E, Fischer P, et al. What makes good synthetic training data for learning disparity and optical flow estimation? Int J Comput Vis, 2018, 126: 942\u2013960","journal-title":"Int J Comput Vis"},{"key":"2803_CR30","unstructured":"Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014. ArXiv: 14126980"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-019-2803-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-019-2803-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-019-2803-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T21:10:37Z","timestamp":1640034637000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-019-2803-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,31]]},"references-count":30,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["2803"],"URL":"https:\/\/doi.org\/10.1007\/s11432-019-2803-x","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,31]]},"assertion":[{"value":"31 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"212101"}}