Removing 3D point cloud occlusion artifacts with generative adversarial networks

Show simple item record

dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta fi
dc.contributor University of Helsinki, Faculty of Science en
dc.contributor Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten sv
dc.contributor.author Väänänen, Pekka
dc.date.issued 2019
dc.identifier.uri URN:NBN:fi:hulib-202003241636
dc.identifier.uri http://hdl.handle.net/10138/313581
dc.description.abstract Real-world locations can be reconstructed as digital 3D models using 3D scanning. The scans often suffer from missing surface regions caused by occlusions and poor scanning geometry, limiting their usefulness for many tasks. We present an automated system that repairs small missing regions using generative adversarial networks (GANs). The system operates on heightmaps of small round surface patches distributed around the missing region. A neural network model predicts a complete plausible surface for each corrupted patch, which is then integrated to the scan. In addition to geometry, surface colors are also generated. Encouraging results are found in the color reconstruction task, but the output geometry is not clearly superior to the results of a simpler baseline spline model. en
dc.language.iso eng
dc.publisher Helsingin yliopisto fi
dc.publisher University of Helsinki en
dc.publisher Helsingfors universitet sv
dc.title Removing 3D point cloud occlusion artifacts with generative adversarial networks en
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.type.ontasot pro gradu-avhandlingar sv
dc.subject.discipline Tietojenkäsittelytiede und
dct.identifier.urn URN:NBN:fi:hulib-202003241636

Files in this item

Files Size Format View
Vaananen_Pekka_Pro_gradu_2019.pdf 12.22Mb application/pdf View/Open

This item appears in the following Collection(s)

Show simple item record