Thesis: Machine Learning for Segmentation and Volume Analysis of 3D Point Clouds
1 week ago
High Level Description
Laser scanning of industrial environments produces very large point clouds, often with tens of millions of points. In a grinding mill, the raw data captures not only the ore mass but also walls, equipment, personnel, and parts of the surroundings, making the input noisy and complex. The ore may be unevenly distributed, exhibit a clear slope, and include steel/stone balls that must be considered part of the ore mass. For operational analysis and fill-level estimation, methods are needed that automatically isolate the ore from other structures and enable robust computation of volume and slope. This thesis addresses these challenges by combining 3D analysis with machine learning to build a practically useful processing pipeline.
Project Description
The thesis will focus on:
- Developing algorithms to segment point clouds and separate ore from surrounding
structures. - Computing volume and slope angles of the segmented ore mass.
- Exploring machine learning and computer vision to improve segmentation robustness.
- Depending on progress, the project may be extended to identify additional mill components such as lifters, plates, end walls, and grates.
Who are we looking for?
Students in Master's-level programs such as Computer Science, Interaction Design, Engineering Physics, Robotics, or similar, with an interest in computer vision, machine learning, and 3D data processing.
An Exciting Journey with Knightec Group
Semcon and Knightec have joined forces as Knightec Group. Together, we are Northern Europe's leading strategic partner in product and digital service development. With a unique combination of cross-functional expertise and a holistic business understanding, we help our clients realize their strategies – from idea to complete solution.
Practical Information
This is a Thesis position, located at our office in Umeå or Örnsköldsvik. Start date is in January 2026.
Please submit your application as soon as possible, but no later than If you have any questions, you are welcome to contact Robert Nazaretyan. Note that due to GDPR, we only accept applications through our careers page.
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