Evaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenarios

dc.contributor.authorSiddique, Arslanen_US
dc.contributor.authorCorsini, Massimilianoen_US
dc.contributor.authorGanovelli, Fabioen_US
dc.contributor.authorCignoni, Paoloen_US
dc.contributor.editorFrosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanueleen_US
dc.date.accessioned2021-10-25T11:53:45Z
dc.date.available2021-10-25T11:53:45Z
dc.date.issued2021
dc.description.abstractPoint cloud registration is a fundamental task in 3D reconstruction and environment perception. We explore the performance of modern Deep Learning-based registration techniques, in particular Deep Global Registration (DGR) and Learning Multiview Registration (LMVR), on an outdoor real world data consisting of thousands of range maps of a building acquired by a Velodyne LIDAR mounted on a drone. We used these pairwise registration methods in a sequential pipeline to obtain an initial rough registration. The output of this pipeline can be further globally refined. This simple registration pipeline allow us to assess if these modern methods are able to deal with this low quality data. Our experiments demonstrated that, despite some design choices adopted to take into account the peculiarities of the data, more work is required to improve the results of the registration.en_US
dc.description.sectionheadersShort Papers 2: Miscellanea
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20211489
dc.identifier.isbn978-3-03868-165-6
dc.identifier.issn2617-4855
dc.identifier.pages187-191
dc.identifier.urihttps://doi.org/10.2312/stag.20211489
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20211489
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectPerception
dc.titleEvaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenariosen_US
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