{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:46:30Z","timestamp":1774799190288,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 12064004"],"award-info":[{"award-number":["No. 12064004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Science and Technology Program","award":["Guike AD21075020"],"award-info":[{"award-number":["Guike AD21075020"]}]},{"name":"Guangxi Science and Technology Program","award":["Guike AB23075076"],"award-info":[{"award-number":["Guike AB23075076"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18492-6","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T06:02:36Z","timestamp":1707717756000},"page":"75171-75194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-scale inputs and context-aware aggregation network for stereo matching"],"prefix":"10.1007","volume":"83","author":[{"given":"Liqing","family":"Shi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-3340","authenticated-orcid":false,"given":"Taiping","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Gengshen","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Minghua","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Nuo","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xiangjie","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"18492_CR1","doi-asserted-by":"publisher","unstructured":"Scharstein D, Szeliski R, Zabih R (2001) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In: Proceedings IEEE workshop on stereo and multi-baseline vision (SMBV 2001), pp 131\u2013140, https:\/\/doi.org\/10.1109\/SMBV.2001.988771","DOI":"10.1109\/SMBV.2001.988771"},{"key":"18492_CR2","doi-asserted-by":"publisher","unstructured":"Weber M, Humenberger M, Kubinger W (2009) A very fast census-based stereo matching implementation on a graphics processing unit. In: 2009 IEEE 12th International conference on computer vision workshops, ICCV Workshops, pp 786\u2013793, https:\/\/doi.org\/10.1109\/ICCVW.2009.5457622","DOI":"10.1109\/ICCVW.2009.5457622"},{"key":"18492_CR3","doi-asserted-by":"publisher","unstructured":"Zhang C, Li Z, Cheng Y et\u00a0al (2015) Meshstereo: a global stereo model with mesh alignment regularization for view interpolation. In: 2015 IEEE International conference on computer vision (ICCV), pp 2057\u20132065, https:\/\/doi.org\/10.1109\/ICCV.2015.238","DOI":"10.1109\/ICCV.2015.238"},{"issue":"11","key":"18492_CR4","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.1109\/TPAMI.2017.2766072","volume":"40","author":"T Taniai","year":"2018","unstructured":"Taniai T, Matsushita Y, Sato Y et al (2018) Continuous 3D label stereo matching using local expansion moves. IEEE Trans Pattern Anal Mach Intell 40(11):2725\u20132739. https:\/\/doi.org\/10.1109\/TPAMI.2017.2766072","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"18492_CR5","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","volume":"30","author":"H Hirschmuller","year":"2008","unstructured":"Hirschmuller H (2008) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328\u2013341. https:\/\/doi.org\/10.1109\/TPAMI.2007.1166","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18492_CR6","doi-asserted-by":"publisher","unstructured":"\u017dbontar J, LeCun Y (2015) Computing the stereo matching cost with a convolutional neural network. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1592\u20131599, https:\/\/doi.org\/10.1109\/CVPR.2015.7298767","DOI":"10.1109\/CVPR.2015.7298767"},{"key":"18492_CR7","doi-asserted-by":"publisher","unstructured":"Chen Z, Sun X, Wang L et\u00a0al (2015) A deep visual correspondence embedding model for stereo matching costs. In: 2015 IEEE International conference on computer vision (ICCV), pp 972\u2013980, https:\/\/doi.org\/10.1109\/ICCV.2015.117","DOI":"10.1109\/ICCV.2015.117"},{"key":"18492_CR8","doi-asserted-by":"publisher","unstructured":"Flynn J, Neulander I, Philbin J et\u00a0al (2016) Deep stereo: learning to predict new views from the world\u2019s imagery. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 5515\u20135524, https:\/\/doi.org\/10.1109\/CVPR.2016.595","DOI":"10.1109\/CVPR.2016.595"},{"key":"18492_CR9","doi-asserted-by":"publisher","unstructured":"Zagoruyko S, Komodakis N (2015) Learning to compare image patches via convolutional neural networks. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 4353\u20134361, https:\/\/doi.org\/10.1109\/CVPR.2015.7299064","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"18492_CR10","doi-asserted-by":"publisher","unstructured":"Seki A, Pollefeys M (2017) SGM-Nets: semi-global matching with neural networks. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 6640\u20136649, https:\/\/doi.org\/10.1109\/CVPR.2017.703","DOI":"10.1109\/CVPR.2017.703"},{"key":"18492_CR11","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/978-3-030-01261-8_45","volume-title":"Computer Vision - ECCV 2018","author":"JL Sch\u00f6nberger","year":"2018","unstructured":"Sch\u00f6nberger JL, Sinha SN, Pollefeys M (2018) Learning to fuse proposals from multiple scanline optimizations in semi-global matching. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision - ECCV 2018. Springer International Publishing, Cham, pp 758\u2013775"},{"key":"18492_CR12","doi-asserted-by":"crossref","unstructured":"Mayer N, Ilg E, H\u00e4usser P et\u00a0al (2016) A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 4040\u20134048","DOI":"10.1109\/CVPR.2016.438"},{"key":"18492_CR13","doi-asserted-by":"crossref","unstructured":"Pang J, Sun W, Ren JS et\u00a0al (2017) Cascade residual learning: a two-stage convolutional neural network for stereo matching. In: 2017 IEEE International conference on computer vision workshops (ICCVW), pp 878\u2013886","DOI":"10.1109\/ICCVW.2017.108"},{"key":"18492_CR14","doi-asserted-by":"crossref","unstructured":"Kendall A, Martirosyan H, Dasgupta S et\u00a0al (2017) End-to-end learning of geometry and context for deep stereo regression. In: 2017 IEEE International conference on computer vision (ICCV), pp 66\u201375","DOI":"10.1109\/ICCV.2017.17"},{"key":"18492_CR15","doi-asserted-by":"crossref","unstructured":"Chang JR, Chen YS (2018) Pyramid stereo matching network. In: 2018 IEEE\/CVF Conference on computer vision and pattern recognition, pp 5410\u20135418","DOI":"10.1109\/CVPR.2018.00567"},{"key":"18492_CR16","doi-asserted-by":"crossref","unstructured":"Yang G, Zhao H, Shi J et\u00a0al (2018) SegStereo: exploiting semantic information for disparity estimation. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01234-2_39"},{"key":"18492_CR17","doi-asserted-by":"crossref","unstructured":"Chen S, Xiang Z, Qiao C et al (2021) SGNet: semantics guided deep stereo matching. In: Ishikawa H, Liu CL, Pajdla T et al (eds) Computer Vision - ACCV 2020. Springer International Publishing, Cham, pp 106\u2013122","DOI":"10.1007\/978-3-030-69525-5_7"},{"key":"18492_CR18","doi-asserted-by":"publisher","unstructured":"Wu Z, Wu X, Zhang X et\u00a0al (2019) Semantic stereo matching with pyramid cost volumes. In: 2019 IEEE\/CVF International conference on computer vision (ICCV), pp 7483\u20137492, https:\/\/doi.org\/10.1109\/ICCV.2019.00758","DOI":"10.1109\/ICCV.2019.00758"},{"key":"18492_CR19","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1007\/s11263-019-01287-w","volume":"128","author":"X Song","year":"2020","unstructured":"Song X, Zhao X, Fang L et al (2020) EdgeStereo: an effective multi-task learning network for stereo matching and edge detection. Int J Comput Vision 128:910\u2013930","journal-title":"Int J Comput Vision"},{"key":"18492_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-20873-8_2","volume-title":"Computer Vision - ACCV 2018","author":"X Song","year":"2019","unstructured":"Song X, Zhao X, Hu H et al (2019) EdgeStereo: a context integrated residual pyramid network for stereo matching. In: Jawahar C, Li H, Mori G et al (eds) Computer Vision - ACCV 2018. Springer International Publishing, Cham, pp 20\u201335"},{"key":"18492_CR21","doi-asserted-by":"crossref","unstructured":"Xu B, Xu Y, Yang X et\u00a0al (2021) Bilateral grid learning for stereo matching networks. In: 2021 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 12492\u201312501","DOI":"10.1109\/CVPR46437.2021.01231"},{"key":"18492_CR22","doi-asserted-by":"crossref","unstructured":"Guo X, Yang K, Yang W et\u00a0al (2019) Group-wise correlation stereo network. In: 2019 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 3268\u20133277","DOI":"10.1109\/CVPR.2019.00339"},{"key":"18492_CR23","doi-asserted-by":"crossref","unstructured":"Zhang F, Prisacariu V, Yang R et\u00a0al (2019) GA-Net: guided aggregation net for end-to-end stereo matching. In: 2019 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 185\u2013194","DOI":"10.1109\/CVPR.2019.00027"},{"key":"18492_CR24","doi-asserted-by":"crossref","unstructured":"Xu H, Zhang J (2020) Aanet: adaptive aggregation network for efficient stereo matching. In: 2020 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 1956\u20131965","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"18492_CR25","doi-asserted-by":"crossref","unstructured":"Xu G, Cheng J, Guo P et\u00a0al (2022) Attention concatenation volume for accurate and efficient stereo matching. In: 2022 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 12971\u201312980","DOI":"10.1109\/CVPR52688.2022.01264"},{"key":"18492_CR26","doi-asserted-by":"publisher","unstructured":"Sun H, Han J, Pang Y et\u00a0al (2023) Supervised biadjacency networks for stereo matching. Multimed Tool Appl pp 1\u201326. https:\/\/doi.org\/10.1007\/s11042-023-15362-5","DOI":"10.1007\/s11042-023-15362-5"},{"key":"18492_CR27","doi-asserted-by":"crossref","unstructured":"Song X, Yang G, Zhu X et\u00a0al (2021) AdaStereo: an efficient domain-adaptive stereo matching approach. Int J Comput Vision 130:226\u2013245. https:\/\/api.semanticscholar.org\/CorpusID:245005774","DOI":"10.1007\/s11263-021-01549-6"},{"key":"18492_CR28","doi-asserted-by":"crossref","unstructured":"Xu G, Wang X, Ding X et\u00a0al (2023) Iterative geometry encoding volume for stereo matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 21919\u201321928","DOI":"10.1109\/CVPR52729.2023.02099"},{"key":"18492_CR29","doi-asserted-by":"crossref","unstructured":"Lou J, Liu W, Chen Z et\u00a0al (2023) ELFNet: evidential local-global fusion for stereo matching. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 17784\u201317793","DOI":"10.1109\/ICCV51070.2023.01630"},{"key":"18492_CR30","doi-asserted-by":"publisher","unstructured":"Zhao H, Zhou H, Zhang Y et\u00a0al (2023) High-frequency stereo matching network. In: 2023 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 1327\u20131336, https:\/\/doi.org\/10.1109\/CVPR52729.2023.00134","DOI":"10.1109\/CVPR52729.2023.00134"},{"key":"18492_CR31","doi-asserted-by":"crossref","unstructured":"Chen L, Wang W, Mordohai P (2023) Learning the distribution of errors in stereo matching for joint disparity and uncertainty estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 17235\u201317244","DOI":"10.1109\/CVPR52729.2023.01653"},{"key":"18492_CR32","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on computer vision and pattern recognition, pp 7132\u20137141, https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"18492_CR33","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","volume":"38","author":"AG Roy","year":"2019","unstructured":"Roy AG, Navab N, Wachinger C (2019) Recalibrating fully convolutional networks with spatial and channel \u201csqueeze and excitation\u2019\u2019 blocks. IEEE Trans Med Imaging 38(2):540\u2013549. https:\/\/doi.org\/10.1109\/TMI.2018.2867261","journal-title":"IEEE Trans Med Imaging"},{"key":"18492_CR34","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-030-00928-1_48","volume-title":"Medical image computing and computer assisted intervention - MICCAI 2018","author":"AG Roy","year":"2018","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks. In: Frangi AF, Schnabel JA, Davatzikos C et al (eds) Medical image computing and computer assisted intervention - MICCAI 2018. Springer International Publishing, Cham, pp 421\u2013429"},{"key":"18492_CR35","doi-asserted-by":"publisher","first-page":"34029","DOI":"10.1109\/ACCESS.2020.2973707","volume":"8","author":"P Liu","year":"2020","unstructured":"Liu P, Dou Q, Wang Q et al (2020) An encoder-decoder neural network with 3D squeeze-and-excitation and deep supervision for brain tumor segmentation. IEEE Access 8:34029\u201334037. https:\/\/doi.org\/10.1109\/ACCESS.2020.2973707","journal-title":"IEEE Access"},{"key":"18492_CR36","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L et\u00a0al (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"18492_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et\u00a0al (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"18492_CR38","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV), pp 565\u2013571, https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"18492_CR39","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the KITTI vision benchmark suite. In: 2012 IEEE Conference on computer vision and pattern recognition, pp 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"18492_CR40","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 3061\u20133070","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"18492_CR41","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"18492_CR42","doi-asserted-by":"crossref","unstructured":"Duggal S, Wang S, Ma WC et\u00a0al (2019) Deeppruner: learning efficient stereo matching via differentiable patchmatch. In: 2019 IEEE\/CVF International conference on computer vision (ICCV), pp 4383\u20134392","DOI":"10.1109\/ICCV.2019.00448"},{"key":"18492_CR43","doi-asserted-by":"crossref","unstructured":"Badki A, Troccoli A, Kim K et\u00a0al (2020) Bi3D: stereo depth estimation via binary classifications. In: 2020 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), pp 1597\u20131605","DOI":"10.1109\/CVPR42600.2020.00167"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18492-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18492-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18492-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T02:08:24Z","timestamp":1725329304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18492-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":43,"journal-issue":{"issue":"30","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["18492"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18492-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,12]]},"assertion":[{"value":"15 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}]}}