Thesis: AI Meets 3D - Deep Learning Methods for Real-Time Logistics Applications (Waldkirch (near Freiburg), Waldkirch (near Freiburg), Waldkirch (near Freiburg), Waldkirch )

Summer Semester 2026 – Limited to 6 Months

Automation in logistics increasingly relies on the analysis of 3D data. A central application is the shape and object recognition of shipped goods. However, processing large point clouds presents a major challenge, as millions of measurement points must be reduced to the most relevant information. The aim of this thesis/internship is to research and develop deep learning methods for the efficient reduction and analysis of 3D point clouds. Real-world data and a practical application scenario will be available for evaluation.

Your Tasks:

• Research current deep learning approaches for processing 3D point clouds and their use in shape recognition
• Familiarize yourself with existing technologies for 3D data analysis

• Document Your Findings

• Develop methods to reduce point clouds to the most information-rich points
• Conduct and evaluate experiments
• Degree program in Computer Science, Mathematics, or a comparable field
• Programming experience in Python or C++
• Knowledge of deep learning and 3D data processing
• Good command of written and spoken English
• Independent, structured, and reliable working style
• Contact: Sarah Disch

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