About this role
Summer Semester 2027 – fixed term of 3–6 months
YOUR TASKS
- You review and analyze state-of-the-art methods for generating LiDAR data from camera images and videos, including approaches based on deep learning, diffusion models, neural rendering and depth estimation.
- You develop and evaluate a pipeline for generating LiDAR-like point clouds from synchronized multi-camera systems.
- You adapt and optimize selected methods to reproduce the characteristics of a SICK LiDAR sensor.
- You investigate the potential and limitations of existing RGB-to-LiDAR approaches for industrial applications.
- You evaluate the generated data in terms of geometric accuracy, robustness and suitability for downstream AI applications.
- You document and present your findings and recommendations.
- The exact scope of the project will be tailored to your background and the requirements of your internship, bachelor's thesis or master's thesis.
YOUR PROFILE
- Studies in computer science, robotics, electrical engineering, mathematics or a related field Interest in computer vision, 3D perception and sensor fusion Good programming skills in Python Basic knowledge of machine learning and deep learning Experience with PyTorch or a similar framework is an advantage Ability to work independently and analyze scientific literature Contact: Sarah Disch