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dc.contributor.authorPhang, Jonathan Then Sien
dc.contributor.supervisorHann Limen_US
dc.contributor.supervisorRaymond Chiongen_US
dc.date.accessioned2023-01-03T03:52:53Z
dc.date.available2023-01-03T03:52:53Z
dc.date.issued2022en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/89924
dc.description.abstract

3D point cloud learning using deep learning architecture has become an active research trend due to advancement in 3D acquisition technologies. However, raw point clouds are often incomplete, unstructured, and noisy due to viewpoint occlusion and surface irregularity. 3D modeling task is the focus in this research work for 3D point cloud representation learning and reconstruction due to its importance in vast applications that require a concise and efficient reconstruction and recognition of object model.

en_US
dc.publisherCurtin Universityen_US
dc.title3D Point Cloud Representation Learning and Reconstruction using Vector-based Neural Networken_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentCurtin Malaysiaen_US
curtin.accessStatusFulltext not availableen_US
curtin.facultyCurtin Malaysiaen_US
curtin.contributor.orcidPhang, Jonathan Then Sien [0000-0002-4797-7375]en_US
dc.date.embargoEnd2024-12-16


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