{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:05:58Z","timestamp":1791306358235,"version":"4.3.1"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2015,10,14]],"date-time":"2015-10-14T00:00:00Z","timestamp":1444780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IMobS3 Laboratory of Excellence","award":["ANR-10-LABX-16-01"],"award-info":[{"award-number":["ANR-10-LABX-16-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we introduce a geometric method for 3D reconstruction of the exterior environment using a panoramic microwave radar and a camera. We rely on the complementarity of these two sensors considering the robustness to the environmental conditions and depth detection ability of the radar, on the one hand, and the high spatial resolution of a vision sensor, on the other. Firstly, geometric modeling of each sensor and of the entire system is presented. Secondly, we address the global calibration problem, which consists of finding the exact transformation between the sensors\u2019 coordinate systems. Two implementation methods are proposed and compared, based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required for calibration. This makes the methods flexible and easy to use by a non-expert operator. Finally, we present a very simple, yet robust 3D reconstruction method based on the sensors\u2019 geometry. This method enables one to reconstruct observed features in 3D using one acquisition (static sensor), which is not always met in the state of the art for outdoor scene reconstruction. The proposed methods have been validated with synthetic and real data.<\/jats:p>","DOI":"10.3390\/s151025937","type":"journal-article","created":{"date-parts":[[2015,10,14]],"date-time":"2015-10-14T10:52:42Z","timestamp":1444819962000},"page":"25937-25967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Toward 3D Reconstruction of Outdoor Scenes Using an MMW Radar and a Monocular Vision Sensor"],"prefix":"10.3390","volume":"15","author":[{"given":"Ghina","family":"Natour","sequence":"first","affiliation":[{"name":"Lasmea-UMR UBP-CNRS 6602, Universit\u00e9 Blaise Pascal, Aubi\u00e8re 63170, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Ait-Aider","sequence":"additional","affiliation":[{"name":"Lasmea-UMR UBP-CNRS 6602, Universit\u00e9 Blaise Pascal, Aubi\u00e8re 63170, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raphael","family":"Rouveure","sequence":"additional","affiliation":[{"name":"IRSTEA, Institut National de Recherche en Sciences et Technologies pour l\u2019Environnement et l\u2019Agriculture, Aubi\u00e8re 63170, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fran\u00e7ois","family":"Berry","sequence":"additional","affiliation":[{"name":"Lasmea-UMR UBP-CNRS 6602, Universit\u00e9 Blaise Pascal, Aubi\u00e8re 63170, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrice","family":"Faure","sequence":"additional","affiliation":[{"name":"IRSTEA, Institut National de Recherche en Sciences et Technologies pour l\u2019Environnement et l\u2019Agriculture, Aubi\u00e8re 63170, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1177\/0278364911434148","article-title":"RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments","volume":"31","author":"Henry","year":"2012","journal-title":"Int. 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