乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T21:40:01Z","timestamp":1729806001823,"version":"3.28.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,7,23]],"date-time":"2023-07-23T00:00:00Z","timestamp":1690070400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,23]],"date-time":"2023-07-23T00:00:00Z","timestamp":1690070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11356-4","type":"journal-article","created":{"date-parts":[[2023,7,23]],"date-time":"2023-07-23T11:01:30Z","timestamp":1690110090000},"page":"10905-10923","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge-Aware Spatial Propagation Network for Multi-view Depth Estimation"],"prefix":"10.1007","volume":"55","author":[{"given":"Siyuan","family":"Xu","sequence":"first","affiliation":[]},{"given":"Qingshan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Wanjuan","family":"Su","sequence":"additional","affiliation":[]},{"given":"Wenbing","family":"Tao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,23]]},"reference":[{"key":"11356_CR1","doi-asserted-by":"crossref","unstructured":"Long X, Liu L, Theobalt C, Wang W (2020) Occlusion-aware depth estimation with adaptive normal constraints. In: European conference on computer vision, pp. 640\u2013657. Springer","DOI":"10.1007\/978-3-030-58545-7_37"},{"key":"11356_CR2","doi-asserted-by":"crossref","unstructured":"Zheng E, Dunn E, Jojic V, Frahm J-M (2014) Patchmatch based joint view selection and depthmap estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1510\u20131517","DOI":"10.1109\/CVPR.2014.196"},{"key":"11356_CR3","doi-asserted-by":"crossref","unstructured":"Galliani S, Lasinger K, Schindler K (2015) Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the IEEE international conference on computer vision, pp. 873\u2013881","DOI":"10.1109\/ICCV.2015.106"},{"key":"11356_CR4","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger JL, Zheng E, Frahm J-M, Pollefeys M (2016) Pixelwise view selection for unstructured multi-view stereo. In: European conference on computer vision, pp. 501\u2013518. Springer","DOI":"10.1007\/978-3-319-46487-9_31"},{"key":"11356_CR5","doi-asserted-by":"crossref","unstructured":"Xu Q, Tao W (2019) Multi-scale geometric consistency guided multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 5483\u20135492","DOI":"10.1109\/CVPR.2019.00563"},{"key":"11356_CR6","doi-asserted-by":"crossref","unstructured":"Xu Q, Tao W (2020) Planar prior assisted patchmatch multi-view stereo. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 12516\u201312523","DOI":"10.1609\/aaai.v34i07.6940"},{"key":"11356_CR7","doi-asserted-by":"crossref","unstructured":"Yao Y, Luo Z, Li S, Fang T, Quan L (2018) Mvsnet: Depth inference for unstructured multi-view stereo. In: Proceedings of the European conference on computer vision (ECCV), pp. 767\u2013783","DOI":"10.1007\/978-3-030-01237-3_47"},{"issue":"1","key":"11356_CR8","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s11063-020-10399-1","volume":"53","author":"Z Gao","year":"2021","unstructured":"Gao Z, Li E, Wang Z, Yang G, Lu J, Ouyang B, Xu D, Liang Z (2021) Object reconstruction based on attentive recurrent network from single and multiple images. Neural Process Lett 53(1):653\u2013670","journal-title":"Neural Process Lett"},{"key":"11356_CR9","doi-asserted-by":"crossref","unstructured":"Yao Y, Luo Z, Li S, Shen T, Fang T, Quan L (2019) Recurrent mvsnet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5525\u20135534","DOI":"10.1109\/CVPR.2019.00567"},{"key":"11356_CR10","doi-asserted-by":"crossref","unstructured":"Yang J, Mao W, Alvarez JM, Liu M (2020) Cost volume pyramid based depth inference for multi-view stereo. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 4877\u20134886","DOI":"10.1109\/CVPR42600.2020.00493"},{"key":"11356_CR11","doi-asserted-by":"crossref","unstructured":"Gu X, Fan Z, Zhu S, Dai Z, Tan F, Tan P (2020) Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2495\u20132504","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"11356_CR12","doi-asserted-by":"crossref","unstructured":"Yu Z, Gao S (2020) Fast-mvsnet: sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 1949\u20131958","DOI":"10.1109\/CVPR42600.2020.00202"},{"key":"11356_CR13","doi-asserted-by":"crossref","unstructured":"Kusupati U, Cheng S, Chen R, Su H (2020) Normal assisted stereo depth estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2189\u20132199","DOI":"10.1109\/CVPR42600.2020.00226"},{"key":"11356_CR14","doi-asserted-by":"crossref","unstructured":"Yu Z, Jin L, Gao S (2020) P$$^2$$net: Patch-match and plane-regularization for unsupervised indoor depth estimation. In: Computer Vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXIV 16, pp. 206\u2013222. Springer","DOI":"10.1007\/978-3-030-58586-0_13"},{"key":"11356_CR15","doi-asserted-by":"crossref","unstructured":"Qi X, Liao R, Liu Z, Urtasun R, Jia J (2018) Geonet: geometric neural network for joint depth and surface normal estimation. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 283\u2013291","DOI":"10.1109\/CVPR.2018.00037"},{"key":"11356_CR16","doi-asserted-by":"crossref","unstructured":"Zhao W, Liu S, Wei Y, Guo H, Liu Y-J (2021) A confidence-based iterative solver of depths and surface normals for deep multi-view stereo. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp. 6168\u20136177","DOI":"10.1109\/ICCV48922.2021.00611"},{"key":"11356_CR17","doi-asserted-by":"crossref","unstructured":"Long X, Lin C, Liu L, Li W, Theobalt C, Yang R, Wang W (2021) Adaptive surface normal constraint for depth estimation. arXiv preprint arXiv:2103.15483","DOI":"10.1109\/ICCV48922.2021.01261"},{"key":"11356_CR18","doi-asserted-by":"crossref","unstructured":"Yin W, Liu Y, Shen C, Yan Y (2019) Enforcing geometric constraints of virtual normal for depth prediction. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 5684\u20135693","DOI":"10.1109\/ICCV.2019.00578"},{"issue":"5","key":"11356_CR19","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1007\/s11063-022-10780-2","volume":"54","author":"G-J Yoon","year":"2022","unstructured":"Yoon G-J, Song J, Hong Y-J, Yoon SM (2022) Single image based three-dimensional scene reconstruction using semantic and geometric priors. Neural Process Lett 54(5):3679\u20133694","journal-title":"Neural Process Lett"},{"key":"11356_CR20","doi-asserted-by":"crossref","unstructured":"Song X, Zhao X, Hu H, Fang L (2018) Edgestereo: a context integrated residual pyramid network for stereo matching. In: Asian conference on computer vision, Springer, pp. 20\u201335","DOI":"10.1007\/978-3-030-20873-8_2"},{"key":"11356_CR21","doi-asserted-by":"crossref","unstructured":"Hu J, Ozay M, Zhang Y, Okatani T (2019) Revisiting single image depth estimation: toward higher resolution maps with accurate object boundaries. In: 2019 IEEE winter conference on applications of computer vision (WACV). IEEE, pp. 1043\u20131051","DOI":"10.1109\/WACV.2019.00116"},{"key":"11356_CR22","doi-asserted-by":"crossref","unstructured":"Wang K, Shen S (2018) Mvdepthnet: real-time multiview depth estimation neural network. In: 2018 International conference on 3d vision (3DV). IEEE, pp. 248\u2013257","DOI":"10.1109\/3DV.2018.00037"},{"key":"11356_CR23","doi-asserted-by":"crossref","unstructured":"Khamis S, Fanello S, Rhemann C, Kowdle A, Valentin J, Izadi S (2018) Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction. In: Proceedings of the European conference on computer vision (ECCV), pp. 573\u2013590","DOI":"10.1007\/978-3-030-01267-0_35"},{"issue":"2","key":"11356_CR24","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/TPAMI.2020.3020800","volume":"44","author":"X Qi","year":"2020","unstructured":"Qi X, Liu Z, Liao R, Torr PH, Urtasun R, Jia J (2020) Geonet++: iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation. IEEE Trans Patt Anal Mach Intell 44(2):969\u2013984","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"11356_CR25","unstructured":"Im S, Jeon H-G, Lin S, Kweon (2019) IS DPSNet: end-to-end deep plane sweep stereo. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=ryeYHi0ctQ"},{"key":"11356_CR26","doi-asserted-by":"crossref","unstructured":"Cheng S, Xu Z, Zhu S, Li Z, Li LE, Ramamoorthi R, Su H (2020) Deep stereo using adaptive thin volume representation with uncertainty awareness. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00260"},{"key":"11356_CR27","doi-asserted-by":"crossref","unstructured":"Hou Y, Kannala J, Solin A (2019) Multi-view stereo by temporal nonparametric fusion. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp. 2651\u20132660","DOI":"10.1109\/ICCV.2019.00274"},{"key":"11356_CR28","doi-asserted-by":"crossref","unstructured":"Xu Q, Tao W (2020) Learning inverse depth regression for multi-view stereo with correlation cost volume. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 12508\u201312515","DOI":"10.1609\/aaai.v34i07.6939"},{"key":"11356_CR29","unstructured":"Xu Q, Tao W (2020) Pvsnet: Pixelwise visibility-aware multi-view stereo network. arXiv preprint arXiv:2007.07714"},{"key":"11356_CR30","doi-asserted-by":"publisher","unstructured":"Yang W, Ai X, Yang Z, Xu Y, Zhao Y (2020) Dedge-agmnet: An effective stereo matching network optimized by depth edge auxiliary task. In: Giacomo, G.D., Catal\u00e1, A., Dilkina, B., Milano, M., Barro, S., Bugar\u00edn, A., Lang, J. (eds.) ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on prestigious applications of artificial intelligence (PAIS 2020). Frontiers in artificial intelligence and applications, vol. 325, pp. 2784\u20132791. https:\/\/doi.org\/10.3233\/FAIA200419","DOI":"10.3233\/FAIA200419"},{"key":"11356_CR31","doi-asserted-by":"crossref","unstructured":"Zhu S, Brazil G, Liu X (2020) The edge of depth: explicit constraints between segmentation and depth. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 13116\u201313125","DOI":"10.1109\/CVPR42600.2020.01313"},{"key":"11356_CR32","doi-asserted-by":"publisher","first-page":"107901","DOI":"10.1016\/j.patcog.2021.107901","volume":"115","author":"F Xue","year":"2021","unstructured":"Xue F, Cao J, Zhou Y, Sheng F, Wang Y, Ming A (2021) Boundary-induced and scene-aggregated network for monocular depth prediction. Patt Recognit 115:107901","journal-title":"Patt Recognit"},{"key":"11356_CR33","doi-asserted-by":"publisher","unstructured":"Gallup D, Frahm J-M, Mordohai P, Yang Q, Pollefeys M (2007) Real-time plane-sweeping stereo with multiple sweeping directions. In: 2007 IEEE conference on computer vision and pattern recognition, pp. 1\u20138 . https:\/\/doi.org\/10.1109\/CVPR.2007.383245","DOI":"10.1109\/CVPR.2007.383245"},{"issue":"6","key":"11356_CR34","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny J (1986) A computational approach to edge detection. IEEE Trans Patt Anal Mach Intell PAMI 8(6):679\u2013698. https:\/\/doi.org\/10.1109\/TPAMI.1986.4767851","journal-title":"IEEE Trans Patt Anal Mach Intell PAMI"},{"key":"11356_CR35","unstructured":"Zhang J, Yao Y, Li S, Luo Z, Fang T (2020) Visibility-aware multi-view stereo network. CoRR arXiv:abs\/2008.07928"},{"issue":"10","key":"11356_CR36","doi-asserted-by":"publisher","first-page":"2361","DOI":"10.1109\/TPAMI.2019.2947374","volume":"42","author":"X Cheng","year":"2020","unstructured":"Cheng X, Wang P, Yang R (2020) Learning depth with convolutional spatial propagation network. IEEE Trans Patt Anal Mach Intell 42(10):2361\u20132379","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"11356_CR37","unstructured":"Liu S, De Mello S, Gu J, Zhong G, Yang M, Kautz J (2017) Learning affinity via spatial propagation networks. Advances in Neural Information Processing Systems 2017-December, pp. 1521\u20131531"},{"key":"11356_CR38","doi-asserted-by":"crossref","unstructured":"Dai A, Chang AX, Savva M, Halber M, Funkhouser T, Nie\u00dfner M (2017) Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5828\u20135839","DOI":"10.1109\/CVPR.2017.261"},{"key":"11356_CR39","doi-asserted-by":"crossref","unstructured":"Shotton J, Glocker B, Zach C, Izadi S, Criminisi A, Fitzgibbon A (2013) Scene coordinate regression forests for camera relocalization in rgb-d images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2930\u20132937","DOI":"10.1109\/CVPR.2013.377"},{"key":"11356_CR40","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the kitti dataset. Int J Robot Res 32:1231\u20131237","journal-title":"Int J Robot Res"},{"key":"11356_CR41","unstructured":"Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. In: 28th Annual conference on neural information processing systems 2014, Neural information processing systems foundation, NIPS 2014, pp. 2366\u20132374"},{"key":"11356_CR42","doi-asserted-by":"crossref","unstructured":"Yang Z, Ren Z, Shan Q, Huang Q (2021) MVS2D: efficient multi-view stereo via attention-driven 2D convolutions","DOI":"10.1109\/CVPR52688.2022.00838"},{"key":"11356_CR43","doi-asserted-by":"crossref","unstructured":"Curless B, Levoy M (1996) A volumetric method for building complex models from range images. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques, pp. 303\u2013312","DOI":"10.1145\/237170.237269"},{"key":"11356_CR44","doi-asserted-by":"crossref","unstructured":"Gan Y, Xu X, Sun W, Lin L (2018) Monocular depth estimation with affinity, vertical pooling, and label enhancement. In: Proceedings of the European conference on computer vision (ECCV), pp. 224\u2013239","DOI":"10.1007\/978-3-030-01219-9_14"},{"key":"11356_CR45","doi-asserted-by":"crossref","unstructured":"Fu H, Gong M, Wang C, Batmanghelich K, Tao D (2018) Deep ordinal regression network for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2002\u20132011","DOI":"10.1109\/CVPR.2018.00214"},{"key":"11356_CR46","unstructured":"Lee JH, Han M-K, Ko DW, Suh IH (2019) From big to small: multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326"},{"key":"11356_CR47","unstructured":"Bhat SF, Alhashim I, Wonka P (2021) Adabins: depth estimation using adaptive bins. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 4009\u20134018"},{"key":"11356_CR48","unstructured":"Kim D, Ga W, Ahn P, Joo D, Chun S, Kim J (2022) Global-local path networks for monocular depth estimation with vertical cutdepth. arXiv preprint arXiv:2201.07436"},{"key":"11356_CR49","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International conference on computer vision (ICCV), pp. 9992\u201310002. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"11356_CR50","doi-asserted-by":"crossref","unstructured":"Liu N, Han J (2016) Dhsnet: Deep hierarchical saliency network for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 678\u2013686","DOI":"10.1109\/CVPR.2016.80"},{"issue":"8","key":"11356_CR51","doi-asserted-by":"publisher","first-page":"182103","DOI":"10.1007\/s11432-021-3384-y","volume":"65","author":"C Fang","year":"2022","unstructured":"Fang C, Tian H, Zhang D, Zhang Q, Han J, Han J (2022) Densely nested top-down flows for salient object detection. Science China Inform Sci 65(8):182103","journal-title":"Science China Inform Sci"},{"issue":"27","key":"11356_CR52","doi-asserted-by":"crossref","first-page":"6","DOI":"10.4108\/eai.12-4-2021.169184","volume":"7","author":"K Ramesh","year":"2021","unstructured":"Ramesh K, Kumar GK, Swapna K, Datta D, Rajest SS (2021) A review of medical image segmentation algorithms. EAI Endors Trans Pervas Health Technol 7(27):6\u20136","journal-title":"EAI Endors Trans Pervas Health Technol"},{"key":"11356_CR53","doi-asserted-by":"publisher","first-page":"107562","DOI":"10.1016\/j.patcog.2020.107562","volume":"110","author":"D Zhang","year":"2021","unstructured":"Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y (2021) Cross-modality deep feature learning for brain tumor segmentation. Patt Recognit 110:107562","journal-title":"Patt Recognit"},{"issue":"8","key":"11356_CR54","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","volume":"37","author":"E Gibson","year":"2018","unstructured":"Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Trans Med Imag 37(8):1822\u20131834","journal-title":"IEEE Trans Med Imag"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11356-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11356-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11356-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T21:07:40Z","timestamp":1729804060000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11356-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,23]]},"references-count":54,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11356"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11356-4","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2023,7,23]]},"assertion":[{"value":"8 July 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare to have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}