{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T12:05:55Z","timestamp":1743422755579},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No. 2021YFB3900804","No. 2021YFB3900804"],"award-info":[{"award-number":["No. 2021YFB3900804","No. 2021YFB3900804"]}]},{"name":"National Key Research and Development Program of China","award":["No. 2021YFB3900804","No. 2021YFB3900804"],"award-info":[{"award-number":["No. 2021YFB3900804","No. 2021YFB3900804"]}]},{"name":"Research Fund of Ministry of Education of China and China Mobile","award":["No. MCM20200J01","No. MCM20200J01"],"award-info":[{"award-number":["No. MCM20200J01","No. MCM20200J01"]}]},{"name":"Research Fund of Ministry of Education of China and China Mobile","award":["No. MCM20200J01","No. MCM20200J01"],"award-info":[{"award-number":["No. MCM20200J01","No. MCM20200J01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In the unsupervised domain adaptation (UDA) (Akada et al. Self-supervised learning of domain invariant features for depth estimation, in: 2022 IEEE\/CVF winter conference on applications of computer vision (WACV), pp 3377\u20133387 (2022). <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/doi.org\/10.1109\/WACV51458.2022.00107\">10.1109\/WACV51458.2022.00107<\/jats:ext-link>) depth estimation task, a new adaptive approach is to use the bidirectional transformation network to transfer the style between the target and source domain inputs, and then train the depth estimation network in their respective domains. However, the domain adaptation process and the style transfer may result in defects and biases, often leading to depth holes and instance edge depth missing in the target domain\u2019s depth output. To address these issues, We propose a training network that has been improved in terms of model structure and supervision constraints. First, we introduce a edge-guided self-attention mechanism in the task network of each domain to enhance the network\u2019s attention to high-frequency edge features, maintain clear boundaries and fill in missing areas of depth. Furthermore, we utilize an edge detection algorithm to extract edge features from the input of the target domain. Then we establish edge consistency constraints between inter-domain entities in order to narrow the gap between domains and make domain-to-domain transfers easier. Our experimental demonstrate that our proposed method effectively solve the aforementioned problem, resulting in a higher quality depth map and outperforming existing state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s11063-024-11621-0","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T06:43:55Z","timestamp":1715237035000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Domain Adaptation Depth Estimation Based on Self-attention Mechanism and Edge Consistency Constraints"],"prefix":"10.1007","volume":"56","author":[{"given":"Peng","family":"Guo","sequence":"first","affiliation":[]},{"given":"Shuguo","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Baoguo","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"11621_CR1","doi-asserted-by":"publisher","unstructured":"Akada H, Bhat SF, Alhashim I, Wonka P (2022) Self-supervised learning of domain invariant features for depth estimation. In: 2022 IEEE\/CVF winter conference on applications of computer vision (WACV). pp. 3377\u20133387. https:\/\/doi.org\/10.1109\/WACV51458.2022.00107","DOI":"10.1109\/WACV51458.2022.00107"},{"key":"11621_CR2","doi-asserted-by":"publisher","unstructured":"Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems 27. https:\/\/doi.org\/10.48550\/arXiv.1406.2283","DOI":"10.48550\/arXiv.1406.2283"},{"key":"11621_CR3","doi-asserted-by":"publisher","unstructured":"Atapour-Abarghouei A, Breckon TP (2019) Veritatem dies aperit - temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). pp. 3368\u20133379. https:\/\/doi.org\/10.1109\/CVPR.2019.00349","DOI":"10.1109\/CVPR.2019.00349"},{"key":"11621_CR4","doi-asserted-by":"publisher","unstructured":"Chen X, Chen X, Zha ZJ (2019) Structure-aware residual pyramid network for monocular depth estimation. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI-19. pp. 694\u2013700. https:\/\/doi.org\/10.24963\/ijcai.2019\/98","DOI":"10.24963\/ijcai.2019\/98"},{"key":"11621_CR5","unstructured":"Saxena A, Chung S, Ng A (2005) Learning depth from single monocular images. Advances in neural information processing systems 18"},{"key":"11621_CR6","doi-asserted-by":"publisher","unstructured":"Zhang Y, Funkhouser T (2018) Deep depth completion of a single rgb-d image. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 175\u2013185. https:\/\/doi.org\/10.1109\/cvpr.2018.00026","DOI":"10.1109\/cvpr.2018.00026"},{"key":"11621_CR7","doi-asserted-by":"publisher","unstructured":"Gaidon A, Wang Q, Cabon Y, Vig E (2016) Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4340\u20134349. https:\/\/doi.org\/10.1109\/cvpr.2016.470","DOI":"10.1109\/cvpr.2016.470"},{"key":"11621_CR8","doi-asserted-by":"publisher","unstructured":"Hu J, Zhang Y, Okatani T (2019) Visualization of convolutional neural networks for monocular depth estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 3869\u20133878. https:\/\/doi.org\/10.1109\/iccv.2019.00397","DOI":"10.1109\/iccv.2019.00397"},{"key":"11621_CR9","doi-asserted-by":"publisher","unstructured":"Zhan H, Garg R, Weerasekera CS, Li K, Agarwal H, Reid I (2018) Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 340\u2013349. https:\/\/doi.org\/10.1109\/cvpr.2018.00043","DOI":"10.1109\/cvpr.2018.00043"},{"key":"11621_CR10","doi-asserted-by":"publisher","unstructured":"Garg R, Bg VK, Carneiro G, Reid I (2016) Unsupervised cnn for single view depth estimation: Geometry to the rescue. In: Computer Vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14. pp. 740\u2013756. Springer. https:\/\/doi.org\/10.1007\/978-3-319-46484-8_45","DOI":"10.1007\/978-3-319-46484-8_45"},{"key":"11621_CR11","doi-asserted-by":"publisher","unstructured":"Godard C, Mac\u00a0Aodha O, Firman M, Brostow GJ (2019) Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 3828\u20133838. https:\/\/doi.org\/10.1109\/iccv.2019.00393","DOI":"10.1109\/iccv.2019.00393"},{"key":"11621_CR12","doi-asserted-by":"publisher","unstructured":"Zheng C, Cham TJ, Cai J (2018) T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks. In: Proceedings of the European conference on computer vision (ECCV). pp. 767\u2013783. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_47","DOI":"10.1007\/978-3-030-01234-2_47"},{"key":"11621_CR13","doi-asserted-by":"publisher","unstructured":"Kundu JN, Uppala PK, Pahuja A, Babu RV (2018) Adadepth: Unsupervised content congruent adaptation for depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2656\u20132665 . https:\/\/doi.org\/10.1109\/cvpr.2018.00281","DOI":"10.1109\/cvpr.2018.00281"},{"key":"11621_CR14","doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2223\u20132232. https:\/\/doi.org\/10.1109\/iccv.2017.244","DOI":"10.1109\/iccv.2017.244"},{"key":"11621_CR15","doi-asserted-by":"publisher","unstructured":"Zhao S, Fu H, Gong M, Tao D (2019) Geometry-aware symmetric domain adaptation for monocular depth estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 9788\u20139798. https:\/\/doi.org\/10.1109\/cvpr.2019.01002","DOI":"10.1109\/cvpr.2019.01002"},{"key":"11621_CR16","doi-asserted-by":"publisher","unstructured":"Huang X, Liu MY, Belongie S, Kautz J (2018) Multimodal unsupervised image-to-image translation. In: Proceedings of the European conference on computer vision (ECCV). pp. 172\u2013189. https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"11621_CR17","doi-asserted-by":"publisher","unstructured":"Chen YC, Lin YY, Yang MH, Huang JB (2019) Crdoco: Pixel-level domain transfer with cross-domain consistency. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 1791\u20131800. https:\/\/doi.org\/10.1109\/cvpr.2019.00189","DOI":"10.1109\/cvpr.2019.00189"},{"key":"11621_CR18","doi-asserted-by":"publisher","unstructured":"PNVR K, Zhou H, Jacobs D (2020) Sharingan: Combining synthetic and real data for unsupervised geometry estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 13974\u201313983. https:\/\/doi.org\/10.1109\/cvpr42600.2020.01399","DOI":"10.1109\/cvpr42600.2020.01399"},{"key":"11621_CR19","doi-asserted-by":"publisher","unstructured":"Schwonberg M, Niemeijer J, Term\u00f6hlen JA, Sch\u00e4fer JP, Schmidt NM, Gottschalk H, Fingscheidt T (2023) Survey on unsupervised domain adaptation for semantic segmentation for visual perception in automated driving. IEEE Access. https:\/\/doi.org\/10.1109\/access.2023.3277785","DOI":"10.1109\/access.2023.3277785"},{"key":"11621_CR20","doi-asserted-by":"publisher","unstructured":"Chiou E, Panagiotaki E, Kokkinos I (2022) Beyond deterministic translation for unsupervised domain adaptation. arXiv preprint arXiv:2202.07778 . https:\/\/doi.org\/10.48550\/arXiv.2006.08658","DOI":"10.48550\/arXiv.2006.08658"},{"key":"11621_CR21","doi-asserted-by":"publisher","first-page":"104871","DOI":"10.1016\/j.imavis.2023.104871","volume":"141","author":"PTH Thanh","year":"2024","unstructured":"Thanh PTH, Bui MQV, Nguyen DD, Pham TV, Duy TVT, Naotake N (2024) Transfer multi-source knowledge via scale-aware online domain adaptation in depth estimation for autonomous driving. Image Vis Comput 141:104871. https:\/\/doi.org\/10.1016\/j.imavis.2023.104871","journal-title":"Image Vis Comput"},{"key":"11621_CR22","doi-asserted-by":"publisher","first-page":"8755","DOI":"10.1609\/aaai.v37i7.26053","volume":"37","author":"Y Liao","year":"2023","unstructured":"Liao Y, Zhou W, Yan X, Li Z, Yu Y, Cui S (2023) Geometry-aware network for domain adaptive semantic segmentation. Proc AAAI Conf Artif Intell 37:8755\u20138763. https:\/\/doi.org\/10.1609\/aaai.v37i7.26053","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"11621_CR23","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. https:\/\/doi.org\/10.1007\/s13042-020-01251-y","journal-title":"Int J Mach Learn Cybern"},{"key":"11621_CR24","doi-asserted-by":"publisher","unstructured":"Huang YK, Wu TH, Liu YC, Hsu WH (2019) Indoor depth completion with boundary consistency and self-attention. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops. pp.\u00a00\u20130. https:\/\/doi.org\/10.1109\/iccvw.2019.00137","DOI":"10.1109\/iccvw.2019.00137"},{"key":"11621_CR25","doi-asserted-by":"publisher","unstructured":"Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2019) Free-form image inpainting with gated convolution. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 4471\u20134480 . https:\/\/doi.org\/10.1109\/iccv.2019.00457","DOI":"10.1109\/iccv.2019.00457"},{"key":"11621_CR26","doi-asserted-by":"publisher","unstructured":"Liu W, Rabinovich A, Berg AC (2015) Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579 . https:\/\/doi.org\/10.48550\/arXiv.1506.04579","DOI":"10.48550\/arXiv.1506.04579"},{"key":"11621_CR27","doi-asserted-by":"publisher","unstructured":"Liu X, Xing F, El\u00a0Fakhri G, Woo J (2022) Self-semantic contour adaptation for cross modality brain tumor segmentation. In: 2022 IEEE 19th international symposium on biomedical imaging (ISBI). pp.\u00a01\u20135. IEEE. https:\/\/doi.org\/10.1109\/isbi52829.2022.9761629","DOI":"10.1109\/isbi52829.2022.9761629"},{"key":"11621_CR28","doi-asserted-by":"publisher","first-page":"1615","DOI":"10.1109\/lsp.2021.3092280","volume":"28","author":"Z Tao","year":"2021","unstructured":"Tao Z, Shuguo P, Hui Z, Yingchun S (2021) Dilated u-block for lightweight indoor depth completion with sobel edge. IEEE Signal Process Lett 28:1615\u20131619. https:\/\/doi.org\/10.1109\/lsp.2021.3092280","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"11621_CR29","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/tpami.2020.3020800","volume":"44","author":"X Qi","year":"2022","unstructured":"Qi X, Liu Z, Liao R, Torr PHS, Urtasun R, Jia J (2022) Geonet++: Iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation. IEEE Trans Pattern Anal Mach Intell 44(2):969\u2013984. https:\/\/doi.org\/10.1109\/tpami.2020.3020800","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11621_CR30","doi-asserted-by":"publisher","unstructured":"Camplani M, Salgado L (2012) Efficient spatio-temporal hole filling strategy for kinect depth maps. In: Three-dimensional image processing (3DIP) and applications Ii. vol.\u00a08290, pp. 127\u2013136. SPIE. https:\/\/doi.org\/10.1117\/12.911909","DOI":"10.1117\/12.911909"},{"key":"11621_CR31","doi-asserted-by":"publisher","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3061\u20133070. https:\/\/doi.org\/10.1109\/cvpr.2015.7298925","DOI":"10.1109\/cvpr.2015.7298925"},{"issue":"5","key":"11621_CR32","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. https:\/\/doi.org\/10.1109\/tpami.2008.132","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"11621_CR33","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. IEEE Trans Pattern Anal Mach Intell 38(10):2024\u20132039. https:\/\/doi.org\/10.1109\/tpami.2015.2505283","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11621_CR34","doi-asserted-by":"publisher","unstructured":"Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems 27. https:\/\/doi.org\/10.48550\/arXiv.1406.2283","DOI":"10.48550\/arXiv.1406.2283"},{"key":"11621_CR35","doi-asserted-by":"publisher","unstructured":"Zhao Y, Kong S, Shin D, Fowlkes C (2020) Domain decluttering: Simplifying images to mitigate synthetic-real domain shift and improve depth estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 3330\u20133340. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00339","DOI":"10.1109\/cvpr42600.2020.00339"},{"issue":"3","key":"11621_CR36","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1007\/s11263-022-01718-1","volume":"131","author":"A Lopez-Rodriguez","year":"2022","unstructured":"Lopez-Rodriguez A, Mikolajczyk K (2022) Desc: Domain adaptation for depth estimation via semantic consistency. Int J Comput Vis 131(3):752\u2013771. https:\/\/doi.org\/10.1007\/s11263-022-01718-1","journal-title":"Int J Comput Vis"},{"key":"11621_CR37","doi-asserted-by":"publisher","unstructured":"Liu X, Guo Z, Li S, Xing F, You J, Kuo CCJ, El\u00a0Fakhri G, Woo J (2021) Adversarial unsupervised domain adaptation with conditional and label shift: Infer, align and iterate. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 10367\u201310376 . https:\/\/doi.org\/10.1109\/iccv48922.2021.01020","DOI":"10.1109\/iccv48922.2021.01020"},{"key":"11621_CR38","doi-asserted-by":"publisher","unstructured":"Atapour-Abarghouei A, Breckon TP (2018) Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2800\u20132810. https:\/\/doi.org\/10.1109\/iccv48922.2021.01020","DOI":"10.1109\/iccv48922.2021.01020"},{"key":"11621_CR39","doi-asserted-by":"publisher","unstructured":"Kundu JN, Lakkakula N, Babu RV (2019) Um-adapt: Unsupervised multi-task adaptation using adversarial cross-task distillation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 1436\u20131445. https:\/\/doi.org\/10.1109\/iccv.2019.00152","DOI":"10.1109\/iccv.2019.00152"},{"issue":"11","key":"11621_CR40","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"11621_CR41","doi-asserted-by":"publisher","unstructured":"Chen X, Wang Y, Chen X, Zeng W (2021) S2r-depthnet: Learning a generalizable depth-specific structural representation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 3034\u20133043. https:\/\/doi.org\/10.1109\/cvpr46437.2021.00305","DOI":"10.1109\/cvpr46437.2021.00305"},{"key":"11621_CR42","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30 . https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"11621_CR43","doi-asserted-by":"publisher","first-page":"8811","DOI":"10.1109\/tip.2021.3120670","volume":"30","author":"X Xu","year":"2021","unstructured":"Xu X, Chen Z, Yin F (2021) Multi-scale spatial attention-guided monocular depth estimation with semantic enhancement. IEEE Trans Image Process 30:8811\u20138822. https:\/\/doi.org\/10.1109\/tip.2021.3120670","journal-title":"IEEE Trans Image Process"},{"key":"11621_CR44","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3\u201319. https:\/\/doi.org\/10.48550\/arXiv.1807.06521","DOI":"10.48550\/arXiv.1807.06521"},{"key":"11621_CR45","doi-asserted-by":"publisher","unstructured":"Jaderberg M, Simonyan K, Zisserman A, et\u00a0al. (2015) Spatial transformer networks. Advances in neural information processing systems 28. https:\/\/doi.org\/10.48550\/arXiv.1506.02025","DOI":"10.48550\/arXiv.1506.02025"},{"key":"11621_CR46","doi-asserted-by":"publisher","unstructured":"Xu ZQJ, Zhang Y, Xiao Y (2019) Training behavior of deep neural network in frequency domain. In: Neural Information Processing: 26th international conference, ICONIP 2019, Sydney, NSW, Australia, December 12\u201315, 2019, Proceedings, Part I 26. pp. 264\u2013274. Springer. https:\/\/doi.org\/10.48550\/arXiv.1807.01251","DOI":"10.48550\/arXiv.1807.01251"},{"issue":"5","key":"11621_CR47","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/tpami.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez P, Maire M, Fowlkes C, Malik J (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898\u2013916. https:\/\/doi.org\/10.1109\/tpami.2010.161","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11621_CR48","doi-asserted-by":"publisher","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: international conference on machine learning. pp. 6105\u20136114. PMLR. https:\/\/doi.org\/10.48550\/arXiv.1905.11946","DOI":"10.48550\/arXiv.1905.11946"},{"key":"11621_CR49","doi-asserted-by":"publisher","unstructured":"Zhang Y, Funkhouser T (2018) Deep depth completion of a single rgb-d image. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 175\u2013185. https:\/\/doi.org\/10.1109\/cvpr.2018.00026","DOI":"10.1109\/cvpr.2018.00026"},{"key":"11621_CR50","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.48550\/arXiv.1711.07971","DOI":"10.48550\/arXiv.1711.07971"},{"issue":"1","key":"11621_CR51","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0262-8856(83)90006-9","volume":"1","author":"J Kittler","year":"1983","unstructured":"Kittler J (1983) On the accuracy of the sobel edge detector. Image Vis Comput 1(1):37\u201342. https:\/\/doi.org\/10.1016\/0262-8856(83)90006-9","journal-title":"Image Vis Comput"},{"key":"11621_CR52","doi-asserted-by":"publisher","unstructured":"Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision. pp. 1395\u20131403. https:\/\/doi.org\/10.1109\/iccv.2015.164","DOI":"10.1109\/iccv.2015.164"},{"key":"11621_CR53","doi-asserted-by":"publisher","unstructured":"Liu Y, Cheng MM, Hu X, Wang K, Bai X (2017) Richer convolutional features for edge detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3000\u20133009. https:\/\/doi.org\/10.1109\/cvpr.2017.622","DOI":"10.1109\/cvpr.2017.622"},{"issue":"4","key":"11621_CR54","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/tip.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/tip.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"11621_CR55","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248\u2013255. Ieee . https:\/\/doi.org\/10.1109\/cvpr.2009.5206848","DOI":"10.1109\/cvpr.2009.5206848"},{"key":"11621_CR56","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234\u2013241. Springer. https:\/\/doi.org\/10.1007\/978-3-662-54345-0_3","DOI":"10.1007\/978-3-662-54345-0_3"},{"key":"11621_CR57","doi-asserted-by":"publisher","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. IEEE. https:\/\/doi.org\/10.1109\/cvpr.2012.6248074","DOI":"10.1109\/cvpr.2012.6248074"},{"key":"11621_CR58","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . https:\/\/doi.org\/10.48550\/arxiv.1412.6980","DOI":"10.48550\/arxiv.1412.6980"},{"key":"11621_CR59","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/cvpr.2017.238","DOI":"10.1109\/cvpr.2017.238"},{"key":"11621_CR60","doi-asserted-by":"publisher","unstructured":"Zhou T, Brown M, Snavely N, Lowe DG (2017) Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1851\u20131858. https:\/\/doi.org\/10.1109\/aivr46125.2019.00059","DOI":"10.1109\/aivr46125.2019.00059"},{"key":"11621_CR61","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/cvpr.2017.699","DOI":"10.1109\/cvpr.2017.699"},{"key":"11621_CR62","doi-asserted-by":"publisher","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3213\u20133223. https:\/\/doi.org\/10.1109\/cvpr.2016.350","DOI":"10.1109\/cvpr.2016.350"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11621-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11621-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11621-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:18:57Z","timestamp":1721042337000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11621-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["11621"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11621-0","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,9]]},"assertion":[{"value":"6 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","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 that there is no Conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"170"}}