Lombardi, MarcoSavardi, MattiaSignoroni, AlbertoCabiddu, DanielaSchneider, TeseoAllegra, DarioCatalano, Chiara EvaCherchi, GianmarcoScateni, Riccardo2022-11-082022-11-082022978-3-03868-191-52617-4855https://doi.org/10.2312/stag.20221262https://diglib.eg.org:443/handle/10.2312/stag20221262Nowadays, 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Reconstruction; 3D imaging; Tracking; Artificial intelligence; Hardware -> Emerging tools and methodologiesComputing methodologiesReconstruction3D imagingTrackingArtificial intelligenceHardwareEmerging tools and methodologies10.2312/stag.20221262111-1133 pages