Deep Learning for 3D Computer Vision
vor 2 Wochen
We are currently looking for motivated TUM Master's students who want to do their thesis or research practical in the fields of 3D object detection, tracking and/ or multi-modal sensor fusion (camera + radar).
- Research and analyze the latest deep learning techniques and models for 3D computer vision and scene understanding in the context of autonomous driving.
- Implement state-of-the-art algorithms and models in Python and PyTorch.
- Develop novel approaches for multi-modal sensor fusion, combining camera and radar data for improved object detection and tracking in challenging environments.
- Work with large-scale datasets for 3D object detection and tracking, including KITTI, Waymo, and nuScenes.
- Transform research concepts into working prototypes.
Industry Partner:
- Possibility to write your thesis in collaboration with our industry partner FusionRide
Type:
- Master Thesis, Research Internship (FP) or Interdisciplinary Project (IDP)
Qualification:
- Pursuing a master’s degree in Computer Science, Engineering, Mathematics or a related field
- Strong programming skills in Python
- Prior experience with Deep Learning (preferably Object Detection/Tracking)
- Familiarity with PyTorch or other deep learning libraries and frameworks
- Knowledge of 3D computer vision, sensor fusion, and perception concepts is a plus
- Strong problem-solving and analytical skills, with a passion for innovation and technology
Philipp Wolters
Computer Vision Team at the Chair of Human Machine Communication
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