dc.contributor.author | Garro, Valeria | en_US |
dc.contributor.author | Pintore, Giovanni | en_US |
dc.contributor.author | Ganovelli, Fabio | en_US |
dc.contributor.author | Gobbetti, Enrico | en_US |
dc.contributor.author | Scopigno, Roberto | en_US |
dc.contributor.editor | Matthias Hullin and Marc Stamminger and Tino Weinkauf | en_US |
dc.date.accessioned | 2016-10-10T08:04:06Z | |
dc.date.available | 2016-10-10T08:04:06Z | |
dc.date.issued | 2016 | |
dc.identifier.isbn | 978-3-03868-025-3 | |
dc.identifier.issn | - | |
dc.identifier.uri | http://dx.doi.org/10.2312/vmv.20161339 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vmv20161339 | |
dc.description.abstract | We present a novel algorithm for fast metric reconstruction on mobile devices using a combination of image and inertial acceleration data. In contrast to previous approaches to this problem, our algorithm does not require a long acquisition time or intensive data processing and can be implemented entirely on common IMU-enabled tablet and smartphones. The method recovers real world units by comparing the acceleration values from the inertial sensors with the ones inferred from images. In order to cope with IMU signal noise, we propose a novel RANSAC-like strategy which helps to remove the outliers. We demonstrate the effectiveness and the accuracy of our method through an integrated mobile system returning point clouds in metric scale. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | I.4.8 [Computer Graphics] | |
dc.subject | Image processing and computer vision | |
dc.subject | Scene analysis | |
dc.title | Fast Metric Acquisition with Mobile Devices | en_US |
dc.description.seriesinformation | Vision, Modeling & Visualization | |
dc.description.sectionheaders | Reconstructing and Understanding the World | |
dc.identifier.doi | 10.2312/vmv.20161339 | |
dc.identifier.pages | 29-36 | |