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Specifically, we utilize reference images and their corresponding depth maps to train an efficient rendering module, which synthesizes a variety of photo-realistic images with different virtual depths. By learning from these images, the detector adapts its features to depth variations. Furthermore, we introduce an auxiliary module that guides the network to learn more informative representations from the depth images. Both modules are removed after training, resulting in no additional computational overhead during the final deployment. <\/jats:p>","DOI":"10.1142\/s2811032324400034","type":"journal-article","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T06:05:12Z","timestamp":1706940312000},"source":"Crossref","is-referenced-by-count":0,"title":["Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth"],"prefix":"10.1142","volume":"02","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5069-3587","authenticated-orcid":false,"given":"Chenhang","family":"He","sequence":"first","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2078-4215","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong"}]}],"member":"219","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Y. 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