Deep Tracking for Robust Real-time Object Scanning

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dc.contributor.authorLombardi, Marcoen_US
dc.contributor.authorSavardi, Mattiaen_US
dc.contributor.authorSignoroni, Albertoen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorAllegra, Darioen_US
dc.contributor.editorCatalano, Chiara Evaen_US
dc.contributor.editorCherchi, Gianmarcoen_US
dc.contributor.editorScateni, Riccardoen_US
dc.date.accessioned2022-11-08T11:44:45Z
dc.date.available2022-11-08T11:44:45Z
dc.date.issued2022
dc.description.abstractNowadays, a high-fidelity 3d model representation can be obtained easily by means of handheld optical scanners, which offer a good level of reconstruction quality, portability, and low latency in scan-to-data. However, it is well known that the tracking process can be critical for such devices: sub-optimal lighting conditions, smooth surfaces in the scene, or occluded views and repetitive patterns are all sources of error. In this scenario, recent disruptive technologies such as sparse convolutional neural networks have been tailored to address common problems in 3D vision and analysis. Our work aims to integrate the most promising solutions into an operating framework which can then be used to achieve compelling results in 3D real-time reconstruction. Several scenes from a dataset containing dense views of objects are tested using our proposed pipeline and compared with the current state-of-the-art of online reconstruction.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20221262
dc.identifier.isbn978-3-03868-191-5
dc.identifier.issn2617-4855
dc.identifier.pages111-113
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20221262
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20221262
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Reconstruction; 3D imaging; Tracking; Artificial intelligence; Hardware -> Emerging tools and methodologies
dc.subjectComputing methodologies
dc.subjectReconstruction
dc.subject3D imaging
dc.subjectTracking
dc.subjectArtificial intelligence
dc.subjectHardware
dc.subjectEmerging tools and methodologies
dc.titleDeep Tracking for Robust Real-time Object Scanningen_US
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Italian Chapter Conference 2022 - Smart Tools and Apps in Graphics