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Impact of AI-Based Semantic Segmentation on Point Cloud Surface Reconstruction Quality and Efficiency

dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorLöffler, Maarten
dc.contributor.authorKooij, Gabi van der
dc.date.accessioned2024-07-24T23:03:33Z
dc.date.available2024-07-24T23:03:33Z
dc.date.issued2024
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/46860
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectThis research investigated the effect of using AI-based semantic segmentation on the quality and efficiency of surface reconstruction. The surface reconstruction algorithms Alpha Shapes, Ball Pivoting and Screened Poisson Surface Reconstruction, as well as three segmentation algorithms HDBSCAN, PointNet++ and PointNeXt are evaluated. This research is done in collaboration with the Expert-Team Visualization and Reconstruction (ETVR) of the Netherlands National Police.
dc.titleImpact of AI-Based Semantic Segmentation on Point Cloud Surface Reconstruction Quality and Efficiency
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsAI;Point Cloud;Segmentation;Reconstruction;3D;PointNet
dc.subject.courseuuArtificial Intelligence
dc.thesis.id34867


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