{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:08:42Z","timestamp":1778782122333,"version":"3.51.4"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Sichuan Province Department of Science and Technology","doi-asserted-by":"publisher","award":["2024NSFSC2049"],"award-info":[{"award-number":["2024NSFSC2049"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202208515033"],"award-info":[{"award-number":["202208515033"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114635","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T09:12:52Z","timestamp":1774861972000},"page":"114635","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Bridging local and global representations: An inter-and intra-window based transformer for unsupervised depth completion"],"prefix":"10.1016","volume":"175","author":[{"given":"Tao","family":"Li","sequence":"first","affiliation":[]},{"given":"Xiucheng","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yonghong","family":"Peng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114635_b1","doi-asserted-by":"crossref","unstructured":"Cao, X., Shi, B., Okura, F., Matsushita, Y., 2021. Normal integration via inverse plane fitting with minimum point-to-plane distance. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2382\u20132391.","DOI":"10.1109\/CVPR46437.2021.00241"},{"key":"10.1016\/j.engappai.2026.114635_b2","doi-asserted-by":"crossref","unstructured":"Chen, Z., Long, F., Qiu, Z., Yao, T., Zhou, W., Luo, J., Mei, T., 2023. Anchorformer: Point cloud completion from discriminative nodes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13581\u201313590.","DOI":"10.1109\/CVPR52729.2023.01305"},{"key":"10.1016\/j.engappai.2026.114635_b3","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wang, P., Yang, R., 2018. Depth estimation via affinity learned with convolutional spatial propagation network. In: Proceedings of the European Conference on Computer Vision. pp. 103\u2013119.","DOI":"10.1007\/978-3-030-01270-0_7"},{"key":"10.1016\/j.engappai.2026.114635_b4","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations."},{"key":"10.1016\/j.engappai.2026.114635_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111076","article-title":"CTRL-F: Pairing convolution with transformer for image classification via multi-level feature cross-attention and representation learning fusion","volume":"156","author":"EL-Assiouti","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114635_b6","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.J., 2017. A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 605\u2013613.","DOI":"10.1109\/CVPR.2017.264"},{"issue":"2","key":"10.1016\/j.engappai.2026.114635_b7","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/LRA.2019.2896963","article-title":"Geo-supervised visual depth prediction","volume":"4","author":"Fei","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.engappai.2026.114635_b8","doi-asserted-by":"crossref","unstructured":"Feng, C., Wang, X., Zhang, Y., Zhao, C., Song, M., 2022. CASwin transformer: a hierarchical cross attention transformer for depth completion. In: Proceedings of the IEEE International Conference on Intelligent Transportation Systems. pp. 2836\u20132841.","DOI":"10.1109\/ITSC55140.2022.9922273"},{"issue":"13","key":"10.1016\/j.engappai.2026.114635_b9","doi-asserted-by":"crossref","first-page":"26199","DOI":"10.1109\/JSEN.2025.3572482","article-title":"Self-supervised depth completion with calibration enhancement and normal guidance","volume":"25","author":"Gao","year":"2025","journal-title":"IEEE Sens. J."},{"issue":"15","key":"10.1016\/j.engappai.2026.114635_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfranklin.2024.107112","article-title":"Non-lifted norm optimal iterative learning control for networked dynamical systems: A computationally efficient approach","volume":"361","author":"Gao","year":"2024","journal-title":"J. Franklin Inst."},{"key":"10.1016\/j.engappai.2026.114635_b11","doi-asserted-by":"crossref","unstructured":"Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W., 1992. Surface reconstruction from unorganized points. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques. pp. 71\u201378.","DOI":"10.1145\/133994.134011"},{"key":"10.1016\/j.engappai.2026.114635_b12","doi-asserted-by":"crossref","unstructured":"Huang, Q., Dong, X., Chen, D., Zhou, H., Zhang, W., Yu, N., 2022. Shape-invariant 3D adversarial point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 15335\u201315344.","DOI":"10.1109\/CVPR52688.2022.01490"},{"key":"10.1016\/j.engappai.2026.114635_b13","article-title":"UDDGN: Domain-independent compact boundary learning method for universal diagnosis domain generation","volume":"74","author":"Huang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114635_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111315","article-title":"Lightweight binary convolutional-transformers fusion network for facial expression recognition","volume":"158","author":"Jin","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114635_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111251","article-title":"Multi-axis vision transformer for medical image segmentation","volume":"158","author":"Khan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114635_b16","doi-asserted-by":"crossref","unstructured":"Ku, J., Harakeh, A., Waslander, S.L., 2018. In defense of classical image processing: Fast depth completion on the cpu. In: Proceedings of the 15th Conference on Computer and Robot Vision. pp. 16\u201322.","DOI":"10.1109\/CRV.2018.00013"},{"key":"10.1016\/j.engappai.2026.114635_b17","doi-asserted-by":"crossref","unstructured":"Lai, K., Bo, L., Ren, X., Fox, D., 2011. A large-scale hierarchical multi-view rgb-d object dataset. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 1817\u20131824.","DOI":"10.1109\/ICRA.2011.5980382"},{"key":"10.1016\/j.engappai.2026.114635_b18","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, J., Willette, J., Hwang, S.J., 2022. Mpvit: Multi-path vision transformer for dense prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7287\u20137296.","DOI":"10.1109\/CVPR52688.2022.00714"},{"issue":"6","key":"10.1016\/j.engappai.2026.114635_b19","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11633-023-1458-0","article-title":"Depthformer: Exploiting long-range correlation and local information for accurate monocular depth estimation","volume":"20","author":"Li","year":"2023","journal-title":"Mach. Intell. Res."},{"key":"10.1016\/j.engappai.2026.114635_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110305","article-title":"A transformer-CNN parallel network for image guided depth completion","volume":"150","author":"Li","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114635_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2024.104750","article-title":"ADCV: Unsupervised depth completion employing adaptive depth-based cost volume","volume":"155","author":"Li","year":"2024","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.engappai.2026.114635_b22","doi-asserted-by":"crossref","unstructured":"Lin, Y., Cheng, T., Zhong, Q., Zhou, W., Yang, H., 2022. Dynamic spatial propagation network for depth completion. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 1638\u20131646.","DOI":"10.1609\/aaai.v36i2.20055"},{"key":"10.1016\/j.engappai.2026.114635_b23","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\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.engappai.2026.114635_b24","doi-asserted-by":"crossref","unstructured":"Liu, X., Shao, X., Wang, B., Li, Y., Wang, S., 2022. Graphcspn: Geometry-aware depth completion via dynamic gcns. In: Proceedings of the European Conference on Computer Vision. pp. 90\u2013107.","DOI":"10.1007\/978-3-031-19827-4_6"},{"key":"10.1016\/j.engappai.2026.114635_b25","doi-asserted-by":"crossref","unstructured":"Lopez-Rodriguez, A., Busam, B., Mikolajczyk, K., 2020. Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data. In: Proceedings of the Asian Conference on Computer Vision.","DOI":"10.1007\/978-3-030-69525-5_20"},{"key":"10.1016\/j.engappai.2026.114635_b26","doi-asserted-by":"crossref","unstructured":"Lu, K., Barnes, N., Anwar, S., Zheng, L., 2022. Depth completion auto-encoder. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops. pp. 63\u201373.","DOI":"10.1109\/WACVW54805.2022.00012"},{"key":"10.1016\/j.engappai.2026.114635_b27","doi-asserted-by":"crossref","unstructured":"Lu, Y., Wang, Q., Ma, S., Geng, T., Chen, Y.V., Chen, H., Liu, D., 2023. Transflow: Transformer as flow learner. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18063\u201318073.","DOI":"10.1109\/CVPR52729.2023.01732"},{"key":"10.1016\/j.engappai.2026.114635_b28","doi-asserted-by":"crossref","unstructured":"Ma, F., Cavalheiro, G.V., Karaman, S., 2019. Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera. In: Proceedings of the International Conference on Robotics and Automation. pp. 3288\u20133295.","DOI":"10.1109\/ICRA.2019.8793637"},{"key":"10.1016\/j.engappai.2026.114635_b29","series-title":"CHADET: Cross-hierarchical-attention for depth-completion using unsupervised lightweight transformer","author":"Marsim","year":"2025"},{"key":"10.1016\/j.engappai.2026.114635_b30","doi-asserted-by":"crossref","first-page":"120781","DOI":"10.1109\/ACCESS.2022.3214316","article-title":"SemAttNet: Toward attention-based semantic aware guided depth completion","volume":"10","author":"Nazir","year":"2022","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.engappai.2026.114635_b31","doi-asserted-by":"crossref","first-page":"6613","DOI":"10.1109\/TNNLS.2022.3211929","article-title":"Single-image 3-D reconstruction: Rethinking point cloud deformation","volume":"35","author":"Nguyen","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"3","key":"10.1016\/j.engappai.2026.114635_b32","doi-asserted-by":"crossref","first-page":"4867","DOI":"10.1109\/LRA.2020.3004325","article-title":"Cross-view semantic segmentation for sensing surroundings","volume":"5","author":"Pan","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.engappai.2026.114635_b33","doi-asserted-by":"crossref","unstructured":"Park, J., Joo, K., Hu, Z., Liu, C.K., So Kweon, I., 2020. Non-local spatial propagation network for depth completion. In: Proceedings of the European Conference on Computer Vision. pp. 120\u2013136.","DOI":"10.1007\/978-3-030-58601-0_8"},{"key":"10.1016\/j.engappai.2026.114635_b34","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114635_b35","doi-asserted-by":"crossref","unstructured":"Rho, K., Ha, J., Kim, Y., 2022. Guideformer: Transformers for image guided depth completion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 6250\u20136259.","DOI":"10.1109\/CVPR52688.2022.00615"},{"key":"10.1016\/j.engappai.2026.114635_b36","doi-asserted-by":"crossref","unstructured":"Rim, P., Park, H., Gangopadhyay, S., Zeng, Z., Chung, Y., Wong, A., 2025. Protodepth: Unsupervised continual depth completion with prototypes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 6304\u20136316.","DOI":"10.1109\/CVPR52734.2025.00591"},{"key":"10.1016\/j.engappai.2026.114635_b37","doi-asserted-by":"crossref","unstructured":"Shivakumar, S.S., Nguyen, T., Miller, I.D., Chen, S.W., Kumar, V., Taylor, C.J., 2019. Dfusenet: Deep fusion of rgb and sparse depth information for image guided dense depth completion. In: Proceedings of the IEEE Intelligent Transportation Systems Conference. pp. 13\u201320.","DOI":"10.1109\/ITSC.2019.8917294"},{"key":"10.1016\/j.engappai.2026.114635_b38","doi-asserted-by":"crossref","unstructured":"Shu, C., Yu, K., Duan, Z., Yang, K., 2020. Feature-metric loss for self-supervised learning of depth and egomotion. In: Proceedings of the European Conference on Computer Vision. pp. 572\u2013588.","DOI":"10.1007\/978-3-030-58529-7_34"},{"key":"10.1016\/j.engappai.2026.114635_b39","doi-asserted-by":"crossref","unstructured":"Tang, J., Tian, F.P., An, B., Li, J., Tan, P., 2024. Bilateral propagation network for depth completion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9763\u20139772.","DOI":"10.1109\/CVPR52733.2024.00932"},{"key":"10.1016\/j.engappai.2026.114635_b40","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TIP.2020.3040528","article-title":"Learning guided convolutional network for depth completion","volume":"30","author":"Tang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.engappai.2026.114635_b41","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/adb2ad","article-title":"Efficient feature fusion network for small objects detection of traffic signs based on cross-dimensional and dual-domain information","volume":"36","author":"Tao","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.114635_b42","doi-asserted-by":"crossref","unstructured":"Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B., Shlens, J., 2021. Scaling local self-attention for parameter efficient visual backbones. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12894\u201312904.","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"10.1016\/j.engappai.2026.114635_b43","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114635_b44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, B., Zhang, G., Liu, Q., Gao, T., Dai, Y., 2023. Lrru: Long-short range recurrent updating networks for depth completion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 9422\u20139432.","DOI":"10.1109\/ICCV51070.2023.00864"},{"key":"10.1016\/j.engappai.2026.114635_b45","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L., 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 568\u2013578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.1016\/j.engappai.2026.114635_b46","article-title":"A Sim2Real defect inversion method for real-world energy transportation systems based on intra-and inter-domain interactive learning","author":"Wang","year":"2026","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2026.114635_b47","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, G., Wang, S., Li, B., Liu, Q., Hui, L., Dai, Y., 2024. Improving depth completion via depth feature upsampling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 21104\u201321113.","DOI":"10.1109\/CVPR52733.2024.01994"},{"issue":"2","key":"10.1016\/j.engappai.2026.114635_b48","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1109\/LRA.2021.3058072","article-title":"Learning topology from synthetic data for unsupervised depth completion","volume":"6","author":"Wong","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"2","key":"10.1016\/j.engappai.2026.114635_b49","doi-asserted-by":"crossref","first-page":"3120","DOI":"10.1109\/LRA.2021.3062602","article-title":"An adaptive framework for learning unsupervised depth completion","volume":"6","author":"Wong","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"2","key":"10.1016\/j.engappai.2026.114635_b50","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1109\/LRA.2020.2969938","article-title":"Unsupervised depth completion from visual inertial odometry","volume":"5","author":"Wong","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.engappai.2026.114635_b51","doi-asserted-by":"crossref","unstructured":"Wong, A., Soatto, S., 2021. Unsupervised depth completion with calibrated backprojection layers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 12747\u201312756.","DOI":"10.1109\/ICCV48922.2021.01251"},{"key":"10.1016\/j.engappai.2026.114635_b52","doi-asserted-by":"crossref","unstructured":"Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G., 2022. Vision transformer with deformable attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4794\u20134803.","DOI":"10.1109\/CVPR52688.2022.00475"},{"key":"10.1016\/j.engappai.2026.114635_b53","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"11","key":"10.1016\/j.engappai.2026.114635_b54","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1049\/ipr2.12834","article-title":"Self-supervised depth completion with multi-view geometric constraints","volume":"17","author":"Xiong","year":"2023","journal-title":"IET Image Process."},{"issue":"8","key":"10.1016\/j.engappai.2026.114635_b55","doi-asserted-by":"crossref","first-page":"7078","DOI":"10.1109\/LRA.2024.3418274","article-title":"TM2B: Transformer-based motion-to-box network for 3D single object tracking on point clouds","volume":"9","author":"Xu","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.engappai.2026.114635_b56","doi-asserted-by":"crossref","unstructured":"Yan, Z., Lin, Y., Wang, K., Zheng, Y., Wang, Y., Zhang, Z., Li, J., Yang, J., 2024. Tri-perspective view decomposition for geometry-aware depth completion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4874\u20134884.","DOI":"10.1109\/CVPR52733.2024.00466"},{"key":"10.1016\/j.engappai.2026.114635_b57","doi-asserted-by":"crossref","unstructured":"Yan, Z., Wang, K., Li, X., Zhang, Z., Li, J., Yang, J., 2023. Desnet: Decomposed scale-consistent network for unsupervised depth completion. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 3109\u20133117.","DOI":"10.1609\/aaai.v37i3.25415"},{"key":"10.1016\/j.engappai.2026.114635_b58","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wong, A., Soatto, S., 2019. Dense depth posterior (ddp) from single image and sparse range. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3353\u20133362.","DOI":"10.1109\/CVPR.2019.00347"},{"key":"10.1016\/j.engappai.2026.114635_b59","doi-asserted-by":"crossref","unstructured":"Yue, X., Sun, S., Kuang, Z., Wei, M., Torr, P.H., Zhang, W., Lin, D., 2021. Vision transformer with progressive sampling. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 387\u2013396.","DOI":"10.1109\/ICCV48922.2021.00044"},{"key":"10.1016\/j.engappai.2026.114635_b60","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, X., Poggi, M., Zhu, Z., Huang, G., Mattoccia, S., 2023. Completionformer: Depth completion with convolutions and vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18527\u201318536.","DOI":"10.1109\/CVPR52729.2023.01777"},{"key":"10.1016\/j.engappai.2026.114635_b61","doi-asserted-by":"crossref","first-page":"5264","DOI":"10.1109\/TIP.2021.3079821","article-title":"Adaptive context-aware multi-modal network for depth completion","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114635_b62","doi-asserted-by":"crossref","unstructured":"Zhao, C., Zhang, Y., Poggi, M., Tosi, F., Guo, X., Zhu, Z., Huang, G., Tang, Y., Mattoccia, S., 2022. Monovit: Self-supervised monocular depth estimation with a vision transformer. In: Proceedings of the International Conference on 3D Vision. pp. 668\u2013678.","DOI":"10.1109\/3DV57658.2022.00077"},{"issue":"4","key":"10.1016\/j.engappai.2026.114635_b63","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TCSVT.2023.3312664","article-title":"Sptr: Structure-preserving transformer for unsupervised indoor depth completion","volume":"34","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626009176?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626009176?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:14:06Z","timestamp":1778778846000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626009176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":63,"alternative-id":["S0952197626009176"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114635","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Bridging local and global representations: An inter-and intra-window based transformer for unsupervised depth completion","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114635","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114635"}}