{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:02Z","timestamp":1740202022025,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>In this paper we present a new network architecture, called G-Net, for 3D pose estimation on RGB images which is trained in a weakly supervised manner. We introduce a two step pipeline based on region-based Convolutional neural networks (CNNs) for feature localization, bounding box refinement based on non-maximum-suppression and depth estimation. The G-Net is able to estimate the depth from single monocular images with a self-tuned loss function. The combination of this predicted depth and the presented two-step localization allows the extraction of the 3D pose of the object. We show in experiments that our method achieves good results compared to other state-of-the-art approaches which are trained in a fully supervised manner.<\/jats:p>","DOI":"10.3233\/978-1-61499-918-8-355","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:05Z","timestamp":1740133625000},"source":"Crossref","is-referenced-by-count":0,"title":["Vehicle Pose Estimation Using G-Net: Multi-Class Localization and Depth Estimation"],"prefix":"10.3233","author":[{"family":"Garc&iacute;a L&oacute;pez Javier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Agudo Antonio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Moreno-Noguer Francesc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:02:15Z","timestamp":1740135735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-917-1&spage=355&doi=10.3233\/978-1-61499-918-8-355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-918-8-355","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}