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However, the addition of layers can result in slower convergence and suboptimal performance. To overcome these issues, we introduce a novel architecture employing model distillation, wherein a teacher network enhances the learning process of a preceding student network. To improve network speed, we integrate an ordinal module in the decoder of the teacher network for weight normalization. This module can classify weights and filter out those with the lowest information content. After the weight classification is completed, as the category value increases, the necessity of useful information decreases accordingly. Furthermore, we incorporate a residual stratification module, which adapts 2D image feature extraction methods to 3D depth, facilitating finer, multi\u2010scale feature representation, to expand the receptive field size at each layer of the network, thereby enhancing the accuracy and robustness of depth estimation. Experimental results using the publicly available KITTI dataset demonstrate that the proposed method accelerates network training compared to the benchmark algorithm, reducing the relative squared error by 2.3% and the root\u2010mean\u2010square error by 3.3%, thus validating the effectiveness of our approach.<\/jats:p>","DOI":"10.1049\/ipr2.70286","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:40:27Z","timestamp":1769085627000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Monocular Unsupervised Depth Estimation of Residual Stratification Based on Ordinal Relation Networks"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1785-3222","authenticated-orcid":false,"given":"Ye","family":"Kuang","sequence":"first","affiliation":[{"name":"College of Electrical and Electronic Technology Shanghai University of Engineering Science  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Technology Shanghai University of Engineering Science  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Technology Shanghai University of Engineering Science  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"D.Wofk R.Ranftl M.M\u00fcller andV.Koltun \u201cMonocular Visual\u2010Inertial Depth Estimation \u201dinIEEE International Conference on Robotics and Automation (ICRA)(2023):6095\u20136101.","DOI":"10.1109\/ICRA48891.2023.10161013"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"H.Zhan R.Garg C. 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