{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T09:38:21Z","timestamp":1775381901408,"version":"3.50.1"},"reference-count":55,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Monocular depth estimation is one of the essential tasks in computer vision as it can provide depth information from 2D images and is extremely beneficial for applications such as autonomous driving, robot navigation, etc. Monocular depth estimation has significantly improved over the past couple of years and deep learning-based methods have surpassed traditional and machine learning-based methods. Deep learning-based methods have further been enhanced using transformer and hybrid approaches. This paper first discusses the sensors used for depth estimation and their limitations. Then, we briefly discuss the evolution of depth estimation. Then we dive into the deep learning methods including transformer and CNN-transformer hybrid methods and their limitations. Later, we discuss several methods addressing challenging weather conditions. Finally, we discuss the current trends, challenges and future directions of the transformer and hybrid methods.<\/jats:p>","DOI":"10.2478\/acss-2025-0003","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T04:39:49Z","timestamp":1737693589000},"page":"21-33","source":"Crossref","is-referenced-by-count":2,"title":["Monocular Depth Estimation: A Review on Hybrid Architectures, Transformers and Addressing Adverse Weather Conditions"],"prefix":"10.2478","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0422-1740","authenticated-orcid":false,"given":"Lakindu","family":"Kumara","sequence":"first","affiliation":[{"name":"Informatics Institute of Technology , Colombo , Sri Lanka"}]},{"given":"Nipuna","family":"Senanayake","sequence":"additional","affiliation":[{"name":"Informatics Institute of Technology , Colombo , Sri Lanka"}]},{"given":"Guhanathan","family":"Poravi","sequence":"additional","affiliation":[{"name":"Informatics Institute of Technology , Colombo , Sri Lanka"}]}],"member":"374","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"2026032415333223209_j_acss-2025-0003_ref_001","unstructured":"P. Vyas, C. Saxena, A. Badapanda, and A. Goswami, \u201cOutdoor monocular depth estimation: A research review,\u201d arXiv preprint arXiv:2205.01399, May 2022. https:\/\/doi.org\/10.48550\/arXiv.2205.01399"},{"key":"2026032415333223209_j_acss-2025-0003_ref_002","unstructured":"Q. Li et al., \u201cDeep learning based monocular depth prediction: Datasets, methods and applications,\u201d arXiv preprint arXiv:2011.04123, 2020. https:\/\/arxiv.org\/pdf\/2011.04123"},{"key":"2026032415333223209_j_acss-2025-0003_ref_003","doi-asserted-by":"crossref","unstructured":"A. Masoumian, H. A. Rashwan, J. Cristiano, M. S. Asif, and D. Puig, \u201cMonocular depth estimation using deep learning: A review,\u201d Sensors, vol. 22, no. 14, Art. no. 5353, Jul. 2022. https:\/\/doi.org\/10.3390\/s22145353","DOI":"10.3390\/s22145353"},{"key":"2026032415333223209_j_acss-2025-0003_ref_004","doi-asserted-by":"crossref","unstructured":"Y. Ming, X. Meng, C. Fan, and H. Yu, \u201cDeep learning for monocular depth estimation: A review,\u201d Neurocomputing, vol. 438, pp. 14\u201333, May 2021. https:\/\/doi.org\/10.1016\/j.neucom.2020.12.089","DOI":"10.1016\/j.neucom.2020.12.089"},{"key":"2026032415333223209_j_acss-2025-0003_ref_005","unstructured":"Foresight, \u201cAn overview of autonomous sensors \u2013 LIDAR, RADAR, and cameras,\u201d 2023. [Online]. Available: https:\/\/www.foresightauto.com\/an-overview-of-autonomous-sensors-lidar-radar-and-cameras\/"},{"key":"2026032415333223209_j_acss-2025-0003_ref_006","doi-asserted-by":"crossref","unstructured":"Y. Li and J. Ibanez-Guzman, \u201cLidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems,\u201d IEEE Signal Processing Magazine, vol. 37, no. 4, pp. 50\u201361, Jul. 2020. https:\/\/doi.org\/10.1109\/MSP.2020.2973615","DOI":"10.1109\/MSP.2020.2973615"},{"key":"2026032415333223209_j_acss-2025-0003_ref_007","doi-asserted-by":"crossref","unstructured":"J. Hasch, \u201cDriving towards 2020: Automotive radar technology trends,\u201d in 2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Heidelberg, Germany, Apr. 2015, pp. 1\u20134. https:\/\/doi.org\/10.1109\/ICMIM.2015.7117956","DOI":"10.1109\/ICMIM.2015.7117956"},{"key":"2026032415333223209_j_acss-2025-0003_ref_008","doi-asserted-by":"crossref","unstructured":"C. Zhao, Q. Sun, C. Zhang, Y. Tang, and F. Qian, \u201cMonocular depth estimation based on deep learning: An overview,\u201d Science China Technological Sciences, vol. 63, no. 9, pp. 1612\u20131627, June 2020. https:\/\/doi.org\/10.1007\/s11431-020-1582-8","DOI":"10.1007\/s11431-020-1582-8"},{"key":"2026032415333223209_j_acss-2025-0003_ref_009","unstructured":"A. Saxena, J. Schulte, and A. Y. Ng, \u201cDepth estimation using monocular and stereo cues,\u201d in IJCAI-07, 2007, pp. 2197\u20132203. [Online]. Available: https:\/\/www.ijcai.org\/Proceedings\/07\/Papers\/354.pdf"},{"key":"2026032415333223209_j_acss-2025-0003_ref_010","unstructured":"H. Caesar et al., \u201cnuScenes: A multimodal dataset for autonomous driving,\u201d arXiv preprint arXiv:1903.11027, Mar. 2019. https:\/\/doi.org\/10.48550\/arXiv.1903.11027"},{"key":"2026032415333223209_j_acss-2025-0003_ref_011","doi-asserted-by":"crossref","unstructured":"A. Geiger, P. Lenz, and R. Urtasun, \u201cAre we ready for autonomous driving? The KITTI vision benchmark suite,\u201d in Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, June 2012, pp. 3354\u20133361. https:\/\/doi.org\/10.1109\/CVPR.2012.6248074","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2026032415333223209_j_acss-2025-0003_ref_012","doi-asserted-by":"crossref","unstructured":"G. Yang, X. Song, C. Huang, Z. Deng, J. Shi, and B. Zhou, \u201cDrivingStereo: A large-scale dataset for stereo matching in autonomous driving scenarios,\u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 2019, pp. 899\u2013908. https:\/\/doi.org\/10.1109\/CVPR.2019.00099","DOI":"10.1109\/CVPR.2019.00099"},{"key":"2026032415333223209_j_acss-2025-0003_ref_013","unstructured":"D. Eigen, C. Puhrsch, and R. Fergus, \u201cDepth map prediction from a single image using a multi-scale deep network,\u201d Adv. Neural. Inf. Process. Syst., vol. 27, 2014. https:\/\/doi.org\/10.48550\/arXiv.1406.2283"},{"key":"2026032415333223209_j_acss-2025-0003_ref_014","doi-asserted-by":"crossref","unstructured":"I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, \u201cDeeper depth prediction with fully convolutional residual networks,\u201d in 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, Oct. 2016, pp. 239\u2013248. https:\/\/doi.org\/10.1109\/3DV.2016.32","DOI":"10.1109\/3DV.2016.32"},{"key":"2026032415333223209_j_acss-2025-0003_ref_015","doi-asserted-by":"crossref","unstructured":"B. Li, C. Shen, Y. Dai, A. van den Hengel, and M. He, \u201cDepth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFS,\u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, June 2015, pp. 1119\u20131127. https:\/\/doi.org\/10.1109\/CVPR.2015.7298715","DOI":"10.1109\/CVPR.2015.7298715"},{"key":"2026032415333223209_j_acss-2025-0003_ref_016","unstructured":"I. Alhashim and P. Wonka, \u201cHigh quality monocular depth estimation via transfer learning,\u201d arXiv preprint arXiv:1812.11941, Dec. 2018. https:\/\/doi.org\/10.48550\/arXiv.1812.11941"},{"key":"2026032415333223209_j_acss-2025-0003_ref_017","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, \u201cDensely connected convolutional networks,\u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July 2017, pp. 4700\u20134708. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"2026032415333223209_j_acss-2025-0003_ref_018","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 2016, pp. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2026032415333223209_j_acss-2025-0003_ref_019","doi-asserted-by":"crossref","unstructured":"C.-H. Yeh, Y.-P. Huang, C.-Y. Lin, and C.-Y. Chang, \u201cTransfer2Depth: Dual attention network with transfer learning for monocular depth estimation,\u201d IEEE Access, vol. 8, pp. 86081\u201386090, May 2020. https:\/\/doi.org\/10.1109\/ACCESS.2020.2992815","DOI":"10.1109\/ACCESS.2020.2992815"},{"key":"2026032415333223209_j_acss-2025-0003_ref_020","doi-asserted-by":"crossref","unstructured":"C. Godard, O. Mac Aodha, and G. J. Brostow, \u201cUnsupervised monocular depth estimation with left-right consistency,\u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July 2017, pp. 270\u2013279. https:\/\/doi.org\/10.1109\/CVPR.2017.699","DOI":"10.1109\/CVPR.2017.699"},{"key":"2026032415333223209_j_acss-2025-0003_ref_021","doi-asserted-by":"crossref","unstructured":"R. Garg, V. Kumar B.G., G. Carneiro, and I. Reid, \u201cUnsupervised CNN for single view depth estimation: Geometry to the rescue,\u201d in Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Part VIII 14, Oct. 2016, pp. 740\u2013756. https:\/\/doi.org\/10.1007\/978-3-319-46484-8_45","DOI":"10.1007\/978-3-319-46484-8_45"},{"key":"2026032415333223209_j_acss-2025-0003_ref_022","doi-asserted-by":"crossref","unstructured":"M. Poggi, F. Aleotti, F. Tosi, and S. Mattoccia, \u201cTowards real-time unsupervised monocular depth estimation on CPU,\u201d in 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 2018, pp. 5848\u20135854. https:\/\/doi.org\/10.1109\/IROS.2018.8593814","DOI":"10.1109\/IROS.2018.8593814"},{"key":"2026032415333223209_j_acss-2025-0003_ref_023","doi-asserted-by":"crossref","unstructured":"J. Liu, Q. Li, R. Cao, W. Tang, and G. Qiu, \u201cMiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation,\u201d ISPRS Journal of Photogrammetry and Remote Sensing, vol. 166, pp. 255\u2013267, Aug. 2020. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.06.004","DOI":"10.1016\/j.isprsjprs.2020.06.004"},{"key":"2026032415333223209_j_acss-2025-0003_ref_024","doi-asserted-by":"crossref","unstructured":"C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, \u201cDigging into self-supervised monocular depth estimation,\u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea (South), Oct. 2019, pp. 3828\u20133838. https:\/\/doi.org\/10.1109\/ICCV.2019.00393","DOI":"10.1109\/ICCV.2019.00393"},{"key":"2026032415333223209_j_acss-2025-0003_ref_025","doi-asserted-by":"crossref","unstructured":"J. Jin, B. Tao, X. Qian, J. Hu, and G. Li, \u201cLightweight monocular absolute depth estimation based on attention mechanism,\u201d Journal of Electronic Imaging, vol. 33, no. 2, Mar. 2024, Art. no. 23010. https:\/\/doi.org\/10.1117\/1.JEI.33.2.023010","DOI":"10.1117\/1.JEI.33.2.023010"},{"key":"2026032415333223209_j_acss-2025-0003_ref_026","doi-asserted-by":"crossref","unstructured":"N. Zhang, F. Nex, G. Vosselman, and N. Kerle, \u201cLite-Mono: A lightweight CNN and transformer architecture for self-supervised monocular depth estimation,\u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, June 2023, pp. 18537\u201318546. https:\/\/doi.org\/10.1109\/CVPR52729.2023.01778","DOI":"10.1109\/CVPR52729.2023.01778"},{"key":"2026032415333223209_j_acss-2025-0003_ref_027","doi-asserted-by":"crossref","unstructured":"J. Wang et al., \u201cWeatherDepth: Curriculum contrastive learning for self-supervised depth estimation under adverse weather conditions,\u201d arXiv preprint arXiv:2310.05556, Oct. 2023. https:\/\/doi.org\/10.48550\/arXiv.2310.05556","DOI":"10.1109\/ICRA57147.2024.10611100"},{"key":"2026032415333223209_j_acss-2025-0003_ref_028","doi-asserted-by":"crossref","unstructured":"C. Zhao et al., \u201cMonoViT: Self-supervised monocular depth estimation with a vision transformer,\u201d in 2022 International Conference on 3D Vision (3DV), Prague, Czech Republic, Sep. 2022, pp. 668\u2013678. https:\/\/doi.org\/10.1109\/3DV57658.2022.00077","DOI":"10.1109\/3DV57658.2022.00077"},{"key":"2026032415333223209_j_acss-2025-0003_ref_029","doi-asserted-by":"crossref","unstructured":"M. A. Rahman and S. A. Fattah, \u201c DwinFormer: Dual window transformers for end-to-end monocular depth estimation,\u201d IEEE Sensors Journal, vol. 23, no. 18, Aug. 2023. https:\/\/doi.org\/10.1109\/JSEN.2023.3299782","DOI":"10.1109\/JSEN.2023.3299782"},{"key":"2026032415333223209_j_acss-2025-0003_ref_030","doi-asserted-by":"crossref","unstructured":"G. Manimaran and J. Swaminathan, \u201cFocal-WNet: An architecture unifying convolution and attention for depth estimation,\u201d in 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), Mumbai, India, Apr. 2022, pp. 1\u20137. https:\/\/doi.org\/10.1109\/I2CT54291.2022.9824488","DOI":"10.1109\/I2CT54291.2022.9824488"},{"key":"2026032415333223209_j_acss-2025-0003_ref_031","doi-asserted-by":"crossref","unstructured":"Z. Li, Z. Chen, X. Liu, and J. Jiang, \u201cDepthFormer: Exploiting long-range correlation and local information for accurate monocular depth estimation,\u201d Machine Intelligence Research, vol. 20, no. 6, pp. 837\u2013854, Dec. 2023. https:\/\/doi.org\/10.1007\/s11633-023-1458-0","DOI":"10.1007\/s11633-023-1458-0"},{"key":"2026032415333223209_j_acss-2025-0003_ref_032","unstructured":"A. Dosovitskiy et al., \u201cAn image is worth 16\u00d716 words: Transformers for image recognition at scale,\u201d arXiv preprint arXiv:2010.11929, Oct. 2020. https:\/\/doi.org\/10.48550\/arXiv.2010.11929"},{"key":"2026032415333223209_j_acss-2025-0003_ref_033","doi-asserted-by":"crossref","unstructured":"C. Xia et al., \u201cPCTDepth: Exploiting parallel CNNs and transformer via dual attention for monocular depth estimation,\u201d Neural Processing Letters, vol. 56, no. 2, Feb. 2024, Art. no. 73. https:\/\/doi.org\/10.1007\/s11063-024-11524-0","DOI":"10.1007\/s11063-024-11524-0"},{"key":"2026032415333223209_j_acss-2025-0003_ref_034","doi-asserted-by":"crossref","unstructured":"D. Shim and H. J. Kim, \u201cSwinDepth: Unsupervised depth estimation using monocular sequences via Swin transformer and densely cascaded network,\u201d in 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, May 2023, pp. 4983\u20134990. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10160657","DOI":"10.1109\/ICRA48891.2023.10160657"},{"key":"2026032415333223209_j_acss-2025-0003_ref_035","doi-asserted-by":"crossref","unstructured":"C. Ning and H. Gan, \u201cTrap attention: Monocular depth estimation with manual traps,\u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, June 2023, pp. 5033\u20135043. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00487","DOI":"10.1109\/CVPR52729.2023.00487"},{"key":"2026032415333223209_j_acss-2025-0003_ref_036","doi-asserted-by":"crossref","unstructured":"A. Astudillo, A. Barrera, C. Guindel, A. Al-Kaff, and F. Garc\u00eda, \u201cDAttNet: monocular depth estimation network based on attention mechanisms,\u201d Neural Computing and Applications, vol. 36, no. 7, pp. 3347\u20133356, Dec. 2023. https:\/\/doi.org\/10.1007\/s00521-023-09210-8","DOI":"10.1007\/s00521-023-09210-8"},{"key":"2026032415333223209_j_acss-2025-0003_ref_037","doi-asserted-by":"crossref","unstructured":"A. Agarwal and C. Arora, \u201cAttention attention everywhere: Monocular depth prediction with skip attention,\u201d in Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, Jan. 2023, pp. 5861\u20135870. https:\/\/doi.org\/10.1109\/WACV56688.2023.00581","DOI":"10.1109\/WACV56688.2023.00581"},{"key":"2026032415333223209_j_acss-2025-0003_ref_038","doi-asserted-by":"crossref","unstructured":"W. Zhao, Y. Song, and T. Wang, \u201cSAU-Net: Monocular depth estimation combining multi-scale features and attention mechanisms,\u201d IEEE Access, vol. 11, Dec. 2023, pp. 137734\u2013137746. https:\/\/doi.org\/10.1109\/ACCESS.2023.3339152","DOI":"10.1109\/ACCESS.2023.3339152"},{"key":"2026032415333223209_j_acss-2025-0003_ref_039","doi-asserted-by":"crossref","unstructured":"Z. Liu et al., \u201cSwin transformer: Hierarchical vision transformer using shifted windows,\u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada, Oct. 2021, pp. 10012\u201310022. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2026032415333223209_j_acss-2025-0003_ref_040","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, and T. Brox, \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d in Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015: 18th International Conference, part III 18, Munich, Germany, Oct. 2015, pp. 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2026032415333223209_j_acss-2025-0003_ref_041","doi-asserted-by":"crossref","unstructured":"D. Xing, J. Shen, C. Ho, and A. Tzes, \u201cROIFormer: semantic-aware region of interest transformer for efficient self-supervised monocular depth estimation,\u201d in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 3, 2023, pp. 2983\u20132991. https:\/\/doi.org\/10.1609\/aaai.v37i3.25401","DOI":"10.1609\/aaai.v37i3.25401"},{"key":"2026032415333223209_j_acss-2025-0003_ref_042","doi-asserted-by":"crossref","unstructured":"L. Yan, F. Yu, and C. Dong, \u201cEMTNet: efficient mobile transformer network for real-time monocular depth estimation,\u201d Pattern Analysis and Applications, vol. 26, no. 4, pp. 1833\u20131846, Oct. 2023. https:\/\/doi.org\/10.1007\/s10044-023-01205-4","DOI":"10.1007\/s10044-023-01205-4"},{"key":"2026032415333223209_j_acss-2025-0003_ref_043","doi-asserted-by":"crossref","unstructured":"K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, \u201cGhostNet: More features from cheap operations,\u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, June 2020, pp. 1580\u20131589. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"2026032415333223209_j_acss-2025-0003_ref_044","doi-asserted-by":"crossref","unstructured":"L. Song et al., \u201cSpatial-aware dynamic lightweight self-supervised monocular depth estimation,\u201d IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 883\u2013890, Nov. 2023. https:\/\/doi.org\/10.1109\/LRA.2023.3337991","DOI":"10.1109\/LRA.2023.3337991"},{"key":"2026032415333223209_j_acss-2025-0003_ref_045","doi-asserted-by":"crossref","unstructured":"L. Papa, P. Russo, and I. Amerini, \u201cMETER: a mobile vision transformer architecture for monocular depth estimation,\u201d IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 10, pp. 5882\u20135893, Mar. 2023. https:\/\/doi.org\/10.1109\/TCSVT.2023.3260310","DOI":"10.1109\/TCSVT.2023.3260310"},{"key":"2026032415333223209_j_acss-2025-0003_ref_046","doi-asserted-by":"crossref","unstructured":"Q. Liu and S. Zhou, \u201cLightDepthNet: Lightweight CNN architecture for monocular depth estimation on edge devices,\u201d IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2389\u20132393, Nov. 2023. https:\/\/doi.org\/10.1109\/TCSII.2023.3337369","DOI":"10.1109\/TCSII.2023.3337369"},{"key":"2026032415333223209_j_acss-2025-0003_ref_047","doi-asserted-by":"crossref","unstructured":"M. Tang, Z. Zhao, and J. Qiu, \u201cA foggy weather simulation algorithm for traffic image synthesis based on monocular depth estimation,\u201d Sensors, vol. 24, no. 6, Mar. 2024, Art. no. 1966. https:\/\/doi.org\/10.3390\/s24061966","DOI":"10.3390\/s24061966"},{"key":"2026032415333223209_j_acss-2025-0003_ref_048","doi-asserted-by":"crossref","unstructured":"K. Saunders, G. Vogiatzis, and L. J. Manso, \u201cSelf-supervised monocular depth estimation: Let\u2019s talk about the weather,\u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France, Oct. 2023, pp. 8907\u20138917. https:\/\/doi.org\/10.1109\/ICCV51070.2023.00818","DOI":"10.1109\/ICCV51070.2023.00818"},{"key":"2026032415333223209_j_acss-2025-0003_ref_049","doi-asserted-by":"crossref","unstructured":"M. Tremblay, S. S. Halder, R. de Charette, and J. F. Lalonde, \u201cRain rendering for evaluating and improving robustness to bad weather,\u201d International Journal of Computer Vision, vol. 129, no. 2, pp. 341\u2013360, Feb. 2021. https:\/\/doi.org\/10.1007\/s11263-020-01366-3","DOI":"10.1007\/s11263-020-01366-3"},{"key":"2026032415333223209_j_acss-2025-0003_ref_050","unstructured":"F. Pizzati and R. de Charette, \u201cCoMoGAN: continuous model-guided image-to-image translation\u201d, [Online]. Available: https:\/\/github.com\/cvrits\/CoMoGAN. Accessed on: Jul. 04, 2024."},{"key":"2026032415333223209_j_acss-2025-0003_ref_051","unstructured":"U. Saxena and R. Giriraj, \u201cAutomold--Road-Augmentation-Library,\u201d GitHub, Feb. 12, 2023. [Online]. Available: https:\/\/github.com\/UjjwalSaxena\/Automold--Road-Augmentation-Library"},{"key":"2026032415333223209_j_acss-2025-0003_ref_052","doi-asserted-by":"crossref","unstructured":"X. Huang, P. Wang, X. Cheng, D. Zhou, Q. Geng, and R. Yang, \u201cThe ApolloScape open dataset for autonomous driving and its application,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 10, pp. 2702\u20132719, Oct. 2020. https:\/\/doi.org\/10.1109\/TPAMI.2019.2926463","DOI":"10.1109\/TPAMI.2019.2926463"},{"key":"2026032415333223209_j_acss-2025-0003_ref_053","doi-asserted-by":"crossref","unstructured":"N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, \u201cIndoor segmentation and support inference from RGBD images,\u201d in Computer Vision \u2013 ECCV 2012: 12th European Conference on Computer Vision, Part V 12, Florence, Italy, Oct. 2012, pp. 746\u2013760. https:\/\/doi.org\/10.1007\/978-3-642-33715-4_54","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"2026032415333223209_j_acss-2025-0003_ref_054","doi-asserted-by":"crossref","unstructured":"M. Cordts et al., \u201cThe cityscapes dataset for semantic urban scene understanding,\u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 2016, pp. 3213\u20133223. https:\/\/doi.org\/10.1109\/CVPR.2016.350","DOI":"10.1109\/CVPR.2016.350"},{"key":"2026032415333223209_j_acss-2025-0003_ref_055","unstructured":"A. Saxena, S. Chung, and A. Ng, \u201cLearning depth from single monocular images,\u201d Neural Information Processing Systems (NIPS), vol. 18, pp. 1\u20138, Dec. 2005."}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.sciendo.com\/pdf\/10.2478\/acss-2025-0003","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:40:22Z","timestamp":1774366822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.2478\/acss-2025-0003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,1]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,24]]},"published-print":{"date-parts":[[2025,1,1]]}},"alternative-id":["10.2478\/acss-2025-0003"],"URL":"https:\/\/doi.org\/10.2478\/acss-2025-0003","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,1]]}}}