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While the corresponding research has developed rapidly with the integration of transformer structures, features in 3D are still simply transformed from visual features, resulting in a mismatch between the detection results and the reality. Moreover, most existing methods suffer from the slow convergence speed. To address these issues in monocular 3D object detection, a \u00a0framework, named geometry\u2010guided monocular detection with transformer (GG\u2010Mono), is proposed. It consists of three main components: 1) the mix\u2010feature encoder module that incorporates pretrained depth estimation models to enhance convergence speed and accuracy; 2) the geometry encoding module that supplements hybrid encoding with global geometry data; 3) the GG decoder module that utilizes geometry queries to guide the decoding process. Extensive experiments show that the model outperforms all existing methods in terms of detection accuracy, and achieves 26.88% and 30.65% in average precision of 3D detection box (AP\n                    <jats:sub>3D<\/jats:sub>\n                    ) on the validation dataset and test dataset, respectively, which is 1.88% and 1.81% higher than the baseline, and significantly improved the convergence speed (from 184 to 90 epochs). These facts prove the advantages of the proposed method for monocular 3D object detection.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202500003","type":"journal-article","created":{"date-parts":[[2025,5,11]],"date-time":"2025-05-11T15:38:26Z","timestamp":1746977906000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Geometry\u2010Guided Transformer for Monocular 3D Object Detection"],"prefix":"10.1002","volume":"7","author":[{"given":"Man","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Instrumentation Science and Engineering Harbin Institute of Technology  Harbin Heilongjiang 150001 China"},{"name":"Department of Industrial and Systems Engineering The Hong Kong Polytechnic University  Kowloon Hong Kong SAR 999077 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science Inner Mongolia University  Hohhot Inner Mongolia 010021 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering Harbin Institute of Technology  Harbin Heilongjiang 150001 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9091-6140","authenticated-orcid":false,"given":"Kai\u2010Leung","family":"Yung","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering The Hong Kong Polytechnic University  Kowloon Hong Kong SAR 999077 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5757-7885","authenticated-orcid":false,"given":"Lidong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering The Hong Kong Polytechnic University  Kowloon Hong Kong SAR 999077 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,5,11]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"T.Yin X.Zhou P.Krahenbuhl inProc. 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