Progressive Shape Reconstruction from Raw 3D Point Clouds

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dc.contributor.authorZHAO, Tong
dc.date.accessioned2024-01-08T08:09:05Z
dc.date.available2024-01-08T08:09:05Z
dc.date.issued2023-03-27
dc.description.abstractWith the enthusiasm for digital twins in the fourth industrial revolution, surface reconstruction from raw 3D point clouds is increasingly needed while facing multifaceted challenges. Advanced data acquisition devices makes it possible to obtain 3D point clouds with multi-scale features. Users expect controllable surface reconstruction approaches with meaningful criteria such as preservation of sharp features or satisfactory accuracy-complexity tradeoffs. This thesis addresses these issues by contributing several approaches to the problem of surface reconstruction from defect-laden point clouds. We first propose the notion of progressive discrete domain for global implicit reconstruction approaches that refines and optimizes a discrete 3D domain in accordance to both input and output, and to user-defined criteria. Based on such a domain discretization, we devise a progressive primitive-aware surface reconstruction approach with capacity to refine the implicit function and its representation, in which the most ill-posed parts of the reconstruction problem are postponed to later stages of the reconstruction, and where the fine geometric details are resolved after discovering the topology. Secondly, we contribute a deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point clouds, whose results can be taken as an additional input to consolidate sharp features for the previous reconstruction approach. Finally, we contribute a coarse-to-fine piecewise smooth surface reconstruction approach that proceeds by clustering quadric error metrics. This approach outputs a simplified reconstructed surface mesh, whose vertices are located on sharp features and whose connectivity is solved by a binary problem solver.In summary, this thesis seeks for effective surface reconstruction from a global and progressive perspective. By combining multiple priors and designing meaningful criteria, the contributed approaches can deal with various defects and multi-scale features.en_US
dc.description.sponsorshipThis thesis has been supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.en_US
dc.identifier.citationTong Zhao. Progressive shape reconstruction from raw 3D point clouds. Computational Geometry [cs.CG]. Université Côte d'Azur, 2023. English. ⟨NNT : 2023COAZ4017⟩. ⟨tel-04131762v2⟩en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3543928
dc.language.isoenen_US
dc.publisherUniversité Côte d’Azuren_US
dc.titleProgressive Shape Reconstruction from Raw 3D Point Cloudsen_US
dc.typeThesisen_US
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