Master Thesis “point Cloud Fusion for Industrial
vor 2 Wochen
Master Thesis “Point cloud fusion for industrial inspection of objects with complex shapes”
- Center for Vision, Automation & Control
- The inline computational imaging (ICI) technique of the Competence Unit High-Performance Vision Systems at AIT is designed for 2D and 3D quality inspection of industrial parts (e.g., parts of cars, PCB boards, complex plastic shapes). To acquire the 3D shape of an object, a camera is moved in reference to the object to capture multi-view images. Additionally, an object is illuminated from different angles to gain information about the surface reflectance behaviour and the local surface orientation, down to a micrometre level. We combine data to generate a highly precise 3D point cloud in a high-speed fashion for finding small surface defects such as scratches or cracks.
- Precise registration with a reference scan or model of the inspected object is pivotal for industrial quality inspection and the control of production processes in this context, since registration will allow for both determining the accuracy of a specific inspection method and assessing a manufactured product’s dimensions and its defect areas in a quantitative fashion.
- The objective of this project is to evaluate state-of-the-art point cloud matching algorithms for 3D shapes and incorporate innovative matching algorithms into a novel point cloud matching and registration tool. The master thesis project will further the research in point cloud registration with a reference object for industrial use case of complex 3D shapes, such as products manufactured by die casting or injection moulding.
- For this master’s thesis project, the following specific aims and related tasks are proposed, including their expected outcomes:
- (1) Literature research of the state of the art in point cloud registration methods
- (2) Create a dataset and set up an evaluation framework
- (3) Evaluation of the point cloud matching algorithms using the set-up framework
- (4) Create a point cloud matching and registration tool
- (5) Improving the state-of-the-art. If improvements for current algorithms can be identified during the projects, adapt existing methods.
- This project entails practical programming work and the analysis of state-of-the-art algorithms. Furthermore, it entails hands-on lab work, such as the acquisition of point clouds with our current lab setups. Finally, the identified algorithms will be tested and evaluated against fitting datasets using evaluation metrics.
- We expect that the evaluation of state-of-the-art point cloud alignment methods for 3D point clouds will benefit the insights into the strengths and weaknesses of modern methods for point cloud matching and registration. Developing a novel 3D point cloud fusion based on the findings will improve the quality of current error detection methods for industrial objects with complex 3D shapes.
- Our Competence Unit **High-Performance Vision Systems** at AIT has been active in research for industrial inspection and quality assurance systems for over 20 years, which creates a stimulating and supportive framework for the students working with us - Therefore, you have the unique opportunity to work on an interesting real-world problem that arose within international research projects and ongoing collaborations with industrial partners.
Your qualifications as an
**Ingenious Partner**:
- Ongoing master’s studies in Computer Vision/Image Processing, Computer Science, Physics, Mathematics or similar
- Programming skills in Python are expected. Knowledge of other programming languages is beneficial.
- Experience in image processing, mathematical optimization, and/or algorithms is expected
- Good communications skills and ability to work in a multidisciplinary team are necessary
- Very good command of English (in writing) is mandatory
**What to expect**:
EUR 828;
- gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. You will be part of our international Young AIT network. As a research institution, we are familiar with the supervision and execution of master’s theses, and we are looking forward to supporting you accordingly
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