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- W2893874087 abstract "Geometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results." @default.
- W2893874087 created "2018-10-05" @default.
- W2893874087 creator A5087869252 @default.
- W2893874087 date "2018-07-06" @default.
- W2893874087 modified "2023-09-22" @default.
- W2893874087 title "Geometric Structure Extraction and Reconstruction" @default.
- W2893874087 doi "https://doi.org/10.24442/boristheses.796" @default.
- W2893874087 hasPublicationYear "2018" @default.
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