{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T20:03:31Z","timestamp":1760385811489},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802294"],"award-info":[{"award-number":["61802294"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M633472"],"award-info":[{"award-number":["2018M633472"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11042-020-09879-2","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T10:03:22Z","timestamp":1602756202000},"page":"6311-6328","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Retrieving point cloud models of target objects in a scene from photographed images"],"prefix":"10.1007","volume":"80","author":[{"given":"Nan","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"9879_CR1","doi-asserted-by":"crossref","unstructured":"Achanta R, Estrada F, Wils P et al (2008) Salient region detection and segmentation\u2019. ICVS, Santorini, Greece, pp 66\u201375","DOI":"10.1007\/978-3-540-79547-6_7"},{"key":"9879_CR2","doi-asserted-by":"crossref","unstructured":"Achanta R, Hemami S, Estrada F et al (2009) Frequency-tuned salient region detection. CVPR, Miami, FL, USA, pp 1597\u20131604","DOI":"10.1109\/CVPR.2009.5206596"},{"issue":"12","key":"9879_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2018","unstructured":"Badrinarayanan V et al (2018) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intel 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"key":"9879_CR4","unstructured":"Bradley D, Boubekeur T, Heidrich W (2008) Accurate multi-view reconstruction using robust binocular stereo and surface meshing, IEEE conf. CVPR, Anchorage, AK, USA, pp 1\u20138"},{"key":"9879_CR5","doi-asserted-by":"crossref","unstructured":"Cech J, Sara R (2007) Efficient sampling of disparity space for fast and accurate matching. In: IEEE conference on computer vision and pattern recognition, IEEE, pp 1\u20138","DOI":"10.1109\/CVPR.2007.383355"},{"issue":"4","key":"9879_CR6","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2018) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intel 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"key":"9879_CR7","doi-asserted-by":"crossref","unstructured":"Fan H, Kong D, Li J (2015) Reconstruction of high-resolution Depth Map using Sparse Linear Model. In: Int Conf intelligent Syst research & mechatronics engineering, pp 283\u2013292","DOI":"10.2991\/isrme-15.2015.65"},{"issue":"1-2","key":"9879_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/0600000052","volume":"9","author":"Y Furukawa","year":"2015","unstructured":"Furukawa Y, Hern\u00e1ndez C (2015) Multi-view stereo: a tutorial. Foundations and Trends in Computer Graphics and Vision 9(1-2):1\u2013148","journal-title":"Foundations and Trends in Computer Graphics and Vision"},{"issue":"8","key":"9879_CR9","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","volume":"32","author":"Y Furukawa","year":"2010","unstructured":"Furukawa Y, Ponce J (2010) Accurate, Dense, and Robust Multi-View Stereopsis. IEEE Trans Pattern Anal Mach Intell 32(8):1362\u20131376","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9879_CR10","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE conference on CVPR, Columbus, OH, USA, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"9879_CR11","doi-asserted-by":"crossref","unstructured":"Goesele M, Snavely N, Curless B et al (2007) Multi-view stereo for community photo collections, ICCV, Rio de Janeiro, Brazil, pp 1\u20138","DOI":"10.1109\/ICCV.2007.4408933"},{"issue":"5","key":"9879_CR12","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1109\/TMM.2014.2316145","volume":"16","author":"Y Guo","year":"2014","unstructured":"Guo Y, Sohel F, Bennamoun M, Wan J et al (2014) An accurate and robust range image registration algorithm for 3D object modeling. IEEE Trans Mult 16(5):1377\u20131390","journal-title":"IEEE Trans Mult"},{"key":"9879_CR13","doi-asserted-by":"publisher","unstructured":"Han X, Laga H, Bennamoun M Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era, IEEE Transactions on Pattern Analysis and Machine Intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2019.2954885","DOI":"10.1109\/TPAMI.2019.2954885"},{"key":"9879_CR14","unstructured":"He H, Mckinnon D, Warren M et al (2010) Graphcut-based Interactive Segmentation using Colour and Depth cues, ACRA"},{"key":"9879_CR15","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P et al (2017) Mask r-CNN, ICCV, Venice, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.322"},{"issue":"2","key":"9879_CR16","first-page":"126","volume":"41","author":"X Hou","year":"2016","unstructured":"Hou X, Xing S, Dongyang MA et al (2016) A method of 3D scene reconstruction based on sequence images. Science of Surveying & Mapping 41(2):126\u2013129","journal-title":"Science of Surveying & Mapping"},{"key":"9879_CR17","doi-asserted-by":"crossref","unstructured":"Jiang L, Shi S, Qi X, Jia J (2018) GAL: Geometric adversarial loss for single-view 3D-Object reconstruction. In: Proceedings of the 15th European Conference on Computer vision(ECCV), Munich, Germany","DOI":"10.1007\/978-3-030-01237-3_49"},{"key":"9879_CR18","doi-asserted-by":"crossref","unstructured":"Lasang P, Shen SM, Kumwilaisak W (2015) Combining high resolution color and depth images for dense 3D reconstruction, ICCE, Berlin, Germany, pp 331\u2013334","DOI":"10.1109\/ICCE-Berlin.2014.7034292"},{"key":"9879_CR19","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1109\/TPAMI.2002.1023810","volume":"24","author":"M Lhuillier","year":"2002","unstructured":"Lhuillier M, Quan L (2002) Match propagation for image-based modeling and rendering. IEEE Trans Pattern Anal Mach Intel 24:1140\u20131146","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"key":"9879_CR20","doi-asserted-by":"crossref","unstructured":"Li Z, Wang K, Meng D et al (2016) Multi-view stereo via depth map fusion: a coordinate decent optimization method. Neurocomputing, pp 46\u201361","DOI":"10.1016\/j.neucom.2015.09.109"},{"key":"9879_CR21","doi-asserted-by":"crossref","unstructured":"Li K, Pham T, Zhan H, Reid I (2018) Efficient dense point cloud object reconstruction using deformation vector fields. In: Proceedings of the 15th European conference on computer vision(ECCV), Munich, Germany, pp 497\u2013513","DOI":"10.1007\/978-3-030-01258-8_31"},{"key":"9879_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Cao X, Dai Q et al (2009) Continuous depth estimation for multi-view stereo. In: IEEE conf CVPR, Miami, FL, USA, pp 2121\u20132128","DOI":"10.1109\/CVPR.2009.5206712"},{"key":"9879_CR23","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition, Boston, MA, USA, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9879_CR24","unstructured":"Mandikal P, Murthy N, Agarwal M, Babu RV (2018) 3d-LMNet: Latent embedding matching for accurate and diverse 3D point cloud reconstruction from a single image. In: 29th British machine vision conference (BMVC), Newcastle, UK, pp 662\u2013674"},{"key":"9879_CR25","doi-asserted-by":"crossref","unstructured":"Park JJ, Florence P, Straub J et al (2019) DeepSDF: Learning continuous signed distance functions for shape representation. In: IEEE CVPR, Long Beach, CA, USA, pp 165\u2013174","DOI":"10.1109\/CVPR.2019.00025"},{"issue":"6","key":"9879_CR26","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R et al (2017) Faster R-CNN:, Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intel 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"key":"9879_CR27","doi-asserted-by":"crossref","unstructured":"Rother C, Kolmogorov V, Blake A (2004) \u201cgrabcut\u201d: interactive foreground extraction using iterated graph cuts. ACM SIGGRAPH, Aug ACM, pp 309\u2013314","DOI":"10.1145\/1015706.1015720"},{"key":"9879_CR28","unstructured":"Snavely N Bundler: Structure from motion (SFM) for unordered image collections, http:\/\/www.cs.cornell.edu\/snavely\/bundler\/, accessed 12 July 2018"},{"issue":"3","key":"9879_CR29","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1145\/1141911.1141964","volume":"25","author":"N Snavely","year":"2006","unstructured":"Snavely N, Seitz SM, Szeliski R (2006) Photo tourism: exploring photo collections in 3D. ACM Trans Graph 25(3):835\u2013846","journal-title":"ACM Trans Graph"},{"key":"9879_CR30","doi-asserted-by":"crossref","unstructured":"Tatarchenko M, Dosovitskiy A, Brox T (2016) Multi-view 3D models from single images with a convolutional network. In: Proceedings of the 14th European conference on computer vision(ECCV), Amsterdam, Netherlands, pp 322\u2013337","DOI":"10.1007\/978-3-319-46478-7_20"},{"key":"9879_CR31","doi-asserted-by":"crossref","unstructured":"Wang J, Sun B, Lu Y MVPNEt: multi-view point re-gression networks for 3D object reconstruction from a single image. In: The Thirty-Third AAAI conference on artificial intelligence (AAAI), Honlulu, Hawaii, USA, 2019. arXiv:1811.09410","DOI":"10.1609\/aaai.v33i01.33018949"},{"key":"9879_CR32","unstructured":"Wu C VisualSFM: A Visual Structure From Motion System, http:\/\/homes.cs.washington.edu\/ccwu\/vsfm\/, accessed 20 Oct 2018"},{"key":"9879_CR33","doi-asserted-by":"crossref","unstructured":"Yang Y, Liang Q, Niu L et al (2014) Belief propagation stereo matching algorithm using ground control points. In: Proc of SPIE - the int society for optical engineering, 9069, pp 90690W-90690W-7","DOI":"10.1117\/12.2050266"},{"issue":"11","key":"9879_CR34","first-page":"255","volume":"42","author":"T Yang","year":"2016","unstructured":"Yang T, Tian H, Liu X et al (2016) Research on image segmentation algorithm based on edge detection and otsu. Comput Eng 42(11):255\u2013260","journal-title":"Comput Eng"},{"key":"9879_CR35","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2017.04.015","volume":"251","author":"JQ Yang","year":"2017","unstructured":"Yang JQ, Zhang Q, Cao ZG (2017) Multi-attribute statistics histograms for accurate and robust pairwise registration of range images. Neurocomputing 251:54\u201367","journal-title":"Neurocomputing"},{"key":"9879_CR36","doi-asserted-by":"crossref","unstructured":"Zhang Z, Shan Y (2000) A Progressive Scheme for Stereo Matching, Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, pp 68\u20138","DOI":"10.1007\/3-540-45296-6_5"},{"key":"9879_CR37","doi-asserted-by":"crossref","unstructured":"Zhu S, Xia X, Zhang Q et al (2007) An image segmentation algorithm in image processing based on threshold segmentation. In: Int IEEE Conf on signal-image technologies and internet-based system. IEEE, Shanghai, China, pp 673\u2013678","DOI":"10.1109\/SITIS.2007.116"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09879-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09879-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09879-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T11:55:49Z","timestamp":1669204549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09879-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,15]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9879"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09879-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,15]]},"assertion":[{"value":"20 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}