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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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-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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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
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., vibration, current, voltage), analog and digital signal processing circuits, microcontrollers or SoCs, and power management systems. The board will be optimized for industrial environments, addressing
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model Assists the faculty mentor to program the industrial robot to acquire stereo images of objects from different perspectives using OpenCV libraries Generates 3D point clouds from each set of images
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point cloud analysis: Detection, Location and Segmentation with LiDAR sensors. Calibration of 2D and 3D sensors. Multimodal sensors: cameras, radar, and solid-state LiDAR. Specific Requirements