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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"3D Computer Vision"”.
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Error Assessment In 3D Computer Vision
… and using the basic equations for stereo-vision with established procedures for camera calibration, the error propagation equations for determining both bias and variability in a general 3D position are provided. The results use recent theoretical developments that quantified the bias and …
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Collaborative Augmented Reality
… algorithms and techniques from fields such as 3D computer vision. Various commercial platforms and SDKs are now available that allow developers to quickly develop mobile AR apps requiring minimal understanding of the underlying technology. Much of the focus to date, both in the research and …
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Development and Validation of Feature-Based Vision Algorithms for Autonomous Ship-Deck Landing
This dissertation investigates feature-based computer vision algorithms for ship-deck landing. These algorithms incorporate 2D, 3D, and stereo computer vision techniques. The vision algorithms were validated using camera and computer hardware integrated into a quadrotor aircraft. Each of the …
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Physics based supervised and unsupervised learning of graph structure
… and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss's law, (3) feedback loops, and …
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Robust recovery of piled box-like objects in range images
This work addresses the vision-guided, robotic bin-picking problem, in the context of which a number of piled objects, should be localized, grasped and transferred by a robotic hand from the position they reside, to a specific place defined by the user. We deal in particular with the depalletizing …
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Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
… to learn features from unlabeled point cloud ”3D object” dataset by using part contrasting and object clustering with GNNs. In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a ”part” dataset. Then a contrast learning GNN (ContrastNet) is …
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Harnessing data priors to mitigate 3D data scarcity
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms