Thesis: Downsampling algorithms for 3D point clouds in real-time logistics automation applications*

Summer Semester 2025 – fixed-term for 3-6 Months

In logistics automation, the processing of 3D data plays a crucial role. One application example is shape recognition of shipped objects. The analysis of complex patterns and relationships within large data sets, particularly in 3D point clouds, presents a significant challenge. To improve existing applications and explore new ones, point clouds must be reduced to the points with the highest information content. This work will investigate statistical methods for sampling point clouds. Data and an application example will be available for method evaluation.

YOUR TASKS:

• Research statistical methods for sampling point clouds and their application in shape recognition
• Familiarize yourself with existing technologies for processing 3D data, particularly the analysis of 3D point clouds
• Develop approaches to reduce point clouds, extracting points with the highest information content
• Analyze and discuss your results and document them in detail

YOUR PROFILE:

• You are studying computer science, mathematics, electrical engineering, or a similar field
• You have experience and enjoy programming in Python or C++
• Ideally, you have knowledge of processing and working with 3D data
• You have good English skills
• You stand out for your reliable and structured thinking and working style

YOUR APPLICATION:

• We are looking forward to your online application
• Sarah Disch
• Job-ID 36370 
• All applications will be treated confidentially

*At SICK, we see people, not gender. 

We put great emphasis on diversity, reject discrimination and do not think in categories such as gender, ethnicity, religion, disability, age or sexual identity.

Stichworte: Intern, Internship, Abschlussarbeit