Deep Learning for 3D Computer Vision

vor 2 Wochen


Nuernberg, Deutschland Technische Universität München Vollzeit

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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