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towers; Participation in the elaboration of requirements; Development of a 3D vision system; Processing using artificial intelligence; Fusion of depth maps to obtain point clouds and visualization
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interactive learning objects and activities Create accessible charts/graphs/diagrams Create animations, animated movies and 3D models Communicate regularly with team members, Team Lead, and Project Coordinator
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-varying photogrammetry 3D point clouds of growing plants for high throughput phenotyping applications. A key part of the project is to craft training data for a Deep Learning-based method aimed
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) Application and further development of deep learning methods for automated object recognition and classification in point clouds and 3D data Establishment of a data processing pipeline for the efficient
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Recognition, or text localization Experience with LiDAR and point clouds or other 3D work Comfortable working in a Linux environment Experience with GIT/source control ADDITIONAL APPLICANT INFORMATION
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machine coding. Augmented and virtual reality applications, 360° video, point clouds, and advanced representations such as Gaussian Splatting require a rethinking of compression models to take into account
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heterogeneous geospatial data such as LiDAR point clouds, aerial orthophotos, street‑level imagery, and map/cadastral information. A central requirement is topological correctness—watertight, manifold meshes
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from heterogeneous geospatial data such as LiDAR point clouds, aerial orthophotos, street‑level imagery, and map/cadastral information. A central requirement is topological correctness—watertight
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Pose EstimationStrong background in computer vision and machine learning applied to pose estimation and visual servoing; Experience with OpenCV, PCL (Point Cloud Library), PyTorch/TensorFlow, and 3D
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) at Norwegian University of Life Sciences (NMBU) has a vacant 3-year PhD–position related to developing deep learning models for 3D forest point clouds. The position is part of "SmartForest" (www.smartforest.no