{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T13:45:41Z","timestamp":1768916741299,"version":"3.49.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62102003"],"award-info":[{"award-number":["62102003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2108085QF258"],"award-info":[{"award-number":["2108085QF258"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100022650","name":"Anhui Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022B623"],"award-info":[{"award-number":["2022B623"]}],"id":[{"id":"10.13039\/501100022650","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui University of Science and Technology Graduate Innovation Fund","award":["2022CX2117"],"award-info":[{"award-number":["2022CX2117"]}]},{"name":"University-level general projects of Anhui University of science and technology","award":["xjyb2020-04"],"award-info":[{"award-number":["xjyb2020-04"]}]},{"name":"the University Synergy Innovation Program of Anhui Province","award":["GXXT-2021-006"],"award-info":[{"award-number":["GXXT-2021-006"]}]},{"name":"the University Synergy Innovation Program of Anhui Province","award":["GXXT-2022-038"],"award-info":[{"award-number":["GXXT-2022-038"]}]},{"name":"Central guiding local technology development special funds","award":["202107d06020001"],"award-info":[{"award-number":["202107d06020001"]}]},{"name":"the Institute of Energy, Hefei Comprehensive National Science Center under","award":["21KZS217"],"award-info":[{"award-number":["21KZS217"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Monocular depth estimation (MDE) has made great progress with the development of convolutional neural networks (CNNs). However, these approaches suffer from essential shortsightedness due to the utilization of insufficient feature-based reasoning. To this end, we propose an effective parallel CNNs and Transformer model for MDE via dual attention (PCTDepth). Specifically, we use two stream backbones to extract features, where ResNet and Swin Transformer are utilized to obtain local detail features and global long-range dependencies, respectively. Furthermore, a hierarchical fusion module (HFM) is designed to actively exchange beneficial information for the complementation of each representation during the intermediate fusion. Finally, a dual attention module is incorporated for each fused feature in the decoder stage to improve the accuracy of the model by enhancing inter-channel correlations and focusing on relevant spatial locations. Comprehensive experiments on the KITTI dataset demonstrate that the proposed model consistently outperforms the other state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s11063-024-11524-0","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T14:02:05Z","timestamp":1708956125000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PCTDepth: Exploiting Parallel CNNs and Transformer via Dual Attention for Monocular Depth Estimation"],"prefix":"10.1007","volume":"56","author":[{"given":"Chenxing","family":"Xia","sequence":"first","affiliation":[]},{"given":"Xiuzhen","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Xiuju","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Kuan-Ching","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xianjin","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"11524_CR1","doi-asserted-by":"crossref","unstructured":"Tiwari L, Ji P, Tran QH, Zhuang B, Anand S, Chandraker M (2020) Pseudo rgb-d for self-improving monocular slam and depth prediction. In: Proceedings of the European conference on computer vision, pp 437\u2013455","DOI":"10.1007\/978-3-030-58621-8_26"},{"issue":"4","key":"11524_CR2","doi-asserted-by":"publisher","first-page":"2068","DOI":"10.1109\/TCSVT.2021.3082763","volume":"32","author":"Z Yuan","year":"2022","unstructured":"Yuan Z, Song X, Bai L, Wang Z, Ouyang W (2022) Temporal-channel transformer for 3d lidar-based video object detection for autonomous driving. IEEE Trans Circuits Syst Video Technol 32(4):2068\u20132078","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11524_CR3","doi-asserted-by":"publisher","first-page":"107112","DOI":"10.1016\/j.patcog.2019.107112","volume":"99","author":"H Liu","year":"2020","unstructured":"Liu H, Tang X, Shen S (2020) Depth-map completion for large indoor scene reconstruction. Pattern Recognit 99:107112","journal-title":"Pattern Recognit"},{"issue":"2","key":"11524_CR4","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TCSVT.2020.2986402","volume":"31","author":"S Zhang","year":"2021","unstructured":"Zhang S, Wen L, Lei Z, Li SZ (2021) Refinedet++: single-shot refinement neural network for object detection. IEEE Trans Circuits Syst Video Technol 31(2):674\u2013687","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11524_CR5","first-page":"1","volume":"1","author":"X Bi","year":"2023","unstructured":"Bi X, Chen D, Huang H, Wang S, Zhang H (2023) Combining pixel-level and structure-level adaptation for semantic segmentation. Neural Process Lett 1:1\u201316","journal-title":"Neural Process Lett"},{"issue":"5","key":"11524_CR6","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TPAMI.2008.132","volume":"31","author":"A Saxena","year":"2008","unstructured":"Saxena A, Sun M, Ng AY (2008) Make3d: learning 3d scene structure from a single still image. IEEE Trans Pattern Anal Mach Intell 31(5):824\u2013840","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11524_CR7","first-page":"2366","volume":"27","author":"D Eigen","year":"2014","unstructured":"Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. Adv Neural Inf Process Syst 27:2366\u20132374","journal-title":"Adv Neural Inf Process Syst"},{"key":"11524_CR8","doi-asserted-by":"crossref","unstructured":"Liu M, Salzmann M, He X (2014) Discrete-continuous depth estimation from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 716\u2013723","DOI":"10.1109\/CVPR.2014.97"},{"key":"11524_CR9","doi-asserted-by":"crossref","unstructured":"Liu F, Shen C, Lin G (2015) Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5162\u20135170","DOI":"10.1109\/CVPR.2015.7299152"},{"issue":"11","key":"11524_CR10","doi-asserted-by":"publisher","first-page":"3174","DOI":"10.1109\/TCSVT.2017.2740321","volume":"28","author":"Y Cao","year":"2017","unstructured":"Cao Y, Wu Z, Shen C (2017) Estimating depth from monocular images as classification using deep fully convolutional residual networks. IEEE Trans Circuits Syst Video Technol 28(11):3174\u20133182","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11524_CR11","doi-asserted-by":"publisher","first-page":"9491","DOI":"10.1007\/s11042-014-2130-z","volume":"74","author":"X Wang","year":"2015","unstructured":"Wang X, Hou C, Pu L, Hou Y (2015) A depth estimating method from a single image using foe crf. Multimed Tools Appl 74:9491\u20139506","journal-title":"Multimed Tools Appl"},{"key":"11524_CR12","unstructured":"Lee JH, Han MK, Ko DW, Suh IH (2019) From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326"},{"key":"11524_CR13","unstructured":"Bhat SF, Alhashim I, Wonka P (2021) Adabins: Depth estimation using adaptive bins. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4009\u20134018"},{"key":"11524_CR14","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 international conference on computer vision, pp 5684\u20135693","DOI":"10.1109\/ICCV.2019.00578"},{"key":"11524_CR15","doi-asserted-by":"crossref","unstructured":"Wang P, Shen X, Lin Z, Cohen S, Price B, Yuille AL (2015) Towards unified depth and semantic prediction from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2800\u20132809","DOI":"10.1109\/CVPR.2015.7298897"},{"key":"11524_CR16","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: Proceedings of the IEEE winter conference on applications of computer vision, pp 1043\u20131051","DOI":"10.1109\/WACV.2019.00116"},{"issue":"5","key":"11524_CR17","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/BF00363944","volume":"58","author":"J Aloimonos","year":"1988","unstructured":"Aloimonos J (1988) Shape from texture. Biol Cybern 58(5):345\u2013360","journal-title":"Biol Cybern"},{"key":"11524_CR18","doi-asserted-by":"crossref","unstructured":"Battiato S, Capra A, Curti S, La\u00a0Cascia M (2004) 3d stereoscopic image pairs by depth-map generation. In: Proceedings of the 2nd international symposium on 3D data processing, visualization and transmission, pp 124\u2013131","DOI":"10.1109\/TDPVT.2004.1335185"},{"issue":"7","key":"11524_CR19","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1016\/j.patcog.2007.12.014","volume":"41","author":"AS Malik","year":"2008","unstructured":"Malik AS, Choi T-S (2008) A novel algorithm for estimation of depth map using image focus for 3d shape recovery in the presence of noise. Pattern Recogn 41(7):2200\u20132225","journal-title":"Pattern Recogn"},{"key":"11524_CR20","first-page":"1161","volume":"18","author":"A Saxena","year":"2005","unstructured":"Saxena A, Chung S, Ng A (2005) Learning depth from single monocular images. Adv Neural Inf Process Syst 18:1161\u20131168","journal-title":"Adv Neural Inf Process Syst"},{"key":"11524_CR21","doi-asserted-by":"crossref","unstructured":"Ranftl R, Bochkovskiy A, Koltun V (2021) Vision transformers for dense prediction. In: Proceedings of the IEEE international conference on computer vision, pp 12179\u201312188","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"11524_CR22","doi-asserted-by":"crossref","unstructured":"Yang G, Tang H, Ding M, Sebe N, Ricci E (2021) Transformer-based attention networks for continuous pixel-wise prediction. In: Proceedings of the IEEE international conference on computer vision, pp 16269\u201316279","DOI":"10.1109\/ICCV48922.2021.01596"},{"issue":"11","key":"11524_CR23","doi-asserted-by":"publisher","first-page":"4381","DOI":"10.1109\/TCSVT.2021.3049869","volume":"31","author":"M Song","year":"2021","unstructured":"Song M, Lim S, Kim W (2021) Monocular depth estimation using laplacian pyramid-based depth residuals. IEEE Trans Circuits Syst Video Technol 31(11):4381\u20134393","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11524_CR24","doi-asserted-by":"crossref","unstructured":"Wang L, Zhang J, Wang O, Lin Z, Lu H (2020) Sdc-depth: semantic divide-and-conquer network for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 541\u2013550","DOI":"10.1109\/CVPR42600.2020.00062"},{"issue":"7","key":"11524_CR25","doi-asserted-by":"publisher","first-page":"4841","DOI":"10.1109\/TCSVT.2021.3128505","volume":"32","author":"X Meng","year":"2022","unstructured":"Meng X, Fan C, Ming Y, Yu H (2022) Cornet: context-based ordinal regression network for monocular depth estimation. IEEE Trans Circuits Syst Video Technol 32(7):4841\u20134853","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11524_CR26","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"},{"issue":"19","key":"11524_CR27","doi-asserted-by":"publisher","first-page":"18762","DOI":"10.1109\/JSEN.2022.3199265","volume":"22","author":"S-J Hwang","year":"2022","unstructured":"Hwang S-J, Park S-J, Baek J-H, Kim B (2022) Self-supervised monocular depth estimation using hybrid transformer encoder. IEEE Sens J 22(19):18762\u201318770","journal-title":"IEEE Sens J"},{"key":"11524_CR28","doi-asserted-by":"crossref","unstructured":"Tomar SS, Suin M, Rajagopalan A (2023) Hybrid transformer based feature fusion for self-supervised monocular depth estimation. In: Proceedings of the European conference on computer vision workshops, pp 308\u2013326","DOI":"10.1007\/978-3-031-25063-7_19"},{"key":"11524_CR29","doi-asserted-by":"crossref","unstructured":"Eigen D, Fergus R (2015) Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE international conference on computer vision, pp 2650\u20132658","DOI":"10.1109\/ICCV.2015.304"},{"key":"11524_CR30","doi-asserted-by":"crossref","unstructured":"Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: Proceedings of the 14th international conference on 3D vision, pp 239\u2013248","DOI":"10.1109\/3DV.2016.32"},{"key":"11524_CR31","doi-asserted-by":"crossref","unstructured":"Lee S, Lee J, Kim B, Yi E, Kim J (2021) Patch-wise attention network for monocular depth estimation. In: Proceedings of the AAAI conference on artificial intelligence, pp 1873\u20131881","DOI":"10.1609\/aaai.v35i3.16282"},{"issue":"6","key":"11524_CR32","doi-asserted-by":"publisher","first-page":"4489","DOI":"10.1007\/s11063-021-10608-5","volume":"53","author":"X Xiang","year":"2021","unstructured":"Xiang X, Kong X, Qiu Y, Zhang K, Lv N (2021) Self-supervised monocular trained depth estimation using triplet attention and funnel activation. Neural Process Lett 53(6):4489\u20134506","journal-title":"Neural Process Lett"},{"key":"11524_CR33","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":"11524_CR34","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"11524_CR35","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"11524_CR36","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6881\u20136890","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"11524_CR37","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"11524_CR38","doi-asserted-by":"crossref","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: Proceedings of the IEEE international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"11524_CR39","doi-asserted-by":"crossref","unstructured":"Chen LC, Yang Y, Wang J, Xu W, Yuille AL (2016) Attention to scale: Scale-aware semantic image segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3640\u20133649","DOI":"10.1109\/CVPR.2016.396"},{"key":"11524_CR40","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11524_CR41","doi-asserted-by":"crossref","unstructured":"Zhang H, Dana K, Shi J, Zhang Z, Wang X, Tyagi A, Agrawal A (2018) Context encoding for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7151\u20137160","DOI":"10.1109\/CVPR.2018.00747"},{"key":"11524_CR42","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11534\u201311542","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"11524_CR43","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"11524_CR44","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"11524_CR45","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"11524_CR46","doi-asserted-by":"crossref","unstructured":"Huynh L, Nguyen-Ha P, Matas J, Rahtu E, Heikkil\u00e4 J (2020) Guiding monocular depth estimation using depth-attention volume. In: Proceedings of the European conference on computer vision, pp 581\u2013597","DOI":"10.1007\/978-3-030-58574-7_35"},{"key":"11524_CR47","doi-asserted-by":"crossref","unstructured":"Lee M, Hwang S, Park C, Lee S (2022) Edgeconv with attention module for monocular depth estimation. In: Proceedings of the IEEE winter conference on applications of computer vision, pp 2858\u20132867","DOI":"10.1109\/WACV51458.2022.00242"},{"key":"11524_CR48","doi-asserted-by":"publisher","first-page":"4691","DOI":"10.1109\/TIP.2021.3074306","volume":"30","author":"X Song","year":"2021","unstructured":"Song X, Li W, Zhou D, Dai Y, Fang J, Li H, Zhang L (2021) Mlda-net: multi-level dual attention-based network for self-supervised monocular depth estimation. IEEE Trans Image Process 30:4691\u20134705","journal-title":"IEEE Trans Image Process"},{"key":"11524_CR49","first-page":"1","volume":"1","author":"Z Wang","year":"2022","unstructured":"Wang Z, Dai X, Guo Z, Huang C, Zhang H (2022) Unsupervised monocular depth estimation with channel and spatial attention. IEEE Trans Neural Networks and Learn Syst 1:1\u201311","journal-title":"IEEE Trans Neural Networks and Learn Syst"},{"key":"11524_CR50","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"11524_CR51","doi-asserted-by":"publisher","first-page":"2674","DOI":"10.1109\/TCSVT.2019.2929202","volume":"30","author":"Y Cao","year":"2020","unstructured":"Cao Y, Zhao T, Xian K, Shen C, Cao Z, Xu S (2020) Monocular depth estimation with augmented ordinal depth relationships. IEEE Trans Circuits Syst Video Technol 30(8):2674\u20132682","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11524_CR52","doi-asserted-by":"crossref","unstructured":"Xu D, Ricci E, Ouyang W, Wang X, Sebe N (2017) Multi-scale continuous crfs as sequential deep networks for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5354\u20135362","DOI":"10.1109\/CVPR.2017.25"},{"key":"11524_CR53","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, pp 224\u2013239","DOI":"10.1007\/978-3-030-01219-9_14"},{"key":"11524_CR54","doi-asserted-by":"crossref","unstructured":"Johnston A, Carneiro G (2020) Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4756\u20134765","DOI":"10.1109\/CVPR42600.2020.00481"},{"issue":"5","key":"11524_CR55","first-page":"2673","volume":"44","author":"D Xu","year":"2020","unstructured":"Xu D, Alameda-Pineda X, Ouyang W, Ricci E, Wang X, Sebe N (2020) Probabilistic graph attention network with conditional kernels for pixel-wise prediction. IEEE Trans Pattern Anal Mach Intell 44(5):2673\u20132688","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"11524_CR56","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1109\/TPAMI.2015.2505283","volume":"38","author":"F Liu","year":"2015","unstructured":"Liu F, Shen C, Lin G, Reid I (2015) Learning depth from single monocular images using deep convolutional neural fields. EEE Trans Pattern Anal Mach Intell 38(10):2024\u20132039","journal-title":"EEE Trans Pattern Anal Mach Intell"},{"key":"11524_CR57","doi-asserted-by":"crossref","unstructured":"Godard C, Mac\u00a0Aodha O, Brostow GJ (2017) Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 270\u2013279","DOI":"10.1109\/CVPR.2017.699"},{"key":"11524_CR58","doi-asserted-by":"crossref","unstructured":"Kuznietsov Y, Stuckler J, Leibe B (2017) Semi-supervised deep learning for monocular depth map prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6647\u20136655","DOI":"10.1109\/CVPR.2017.238"},{"key":"11524_CR59","doi-asserted-by":"crossref","unstructured":"Patil V, Sakaridis C, Liniger A, Van\u00a0Gool L (2022) P3depth: monocular depth estimation with a piecewise planarity prior. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1610\u20131621","DOI":"10.1109\/CVPR52688.2022.00166"},{"issue":"11","key":"11524_CR60","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(11):1231\u20131237","journal-title":"Int J Robot Res"},{"key":"11524_CR61","unstructured":"Alhashim I, Wonka P (2018) High quality monocular depth estimation via transfer learning. arXiv preprint arXiv:1812.11941"},{"key":"11524_CR62","doi-asserted-by":"crossref","unstructured":"Chen X, Chen X, Zha ZJ (2019) Structure-aware residual pyramid network for monocular depth estimation. arXiv preprint arXiv:1907.06023","DOI":"10.24963\/ijcai.2019\/98"},{"key":"11524_CR63","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1007\/s13042-020-01251-y","volume":"12","author":"Y Chen","year":"2021","unstructured":"Chen Y, Zhao H, Hu Z, Peng J (2021) Attention-based context aggregation network for monocular depth estimation. Int J Mach Learn Cybern 12:1583\u20131596","journal-title":"Int J Mach Learn Cybern"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11524-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11524-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11524-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T20:28:25Z","timestamp":1715891305000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11524-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":63,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["11524"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11524-0","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"7 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"73"}}