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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



Master Seminar: 3D Shape Matching and Application in Computer Vision (5 ECTS)

Summer Semester 2022, TU München

Organisers: Maolin Gao, Marvin Eisenberger

Please direct questions to 3dsm-ss22@vision.in.tum.de


2022-03-29: The list of papers has been published here.

2022-03-11: After the matching phase, we have some limited vacancies open for application. If you are interested, please send us your transcript (optional CV) until 25.03.2022. We will come back to you after the application deadline asap. Please note due to the limited remaining vacancies, we can only take the most eligible students.

2022-01-28: [preliminary meeting] will take place at 10:00 - 11:00 on 07.02.2022 online via zoom. The slides will be published afterwards. You are encouraged to participate and ask questions directly in the meeting, since it will positively affect the matching process. Please find the zoom link in TUMonline.

Course Description

3D Shape Matching problems are ubiquitous in computer vision, graphics and related fields. Such problems can appear in many different contexts, e.g. shape correspondences, object tracking, 3D reconstruction and interpolation etc., which highlights their high relevance. In this seminar, we will review the classic and recent advances in 3D shape matching, both optimisation-based and machine-learning-based approaches. Students will read a list of selected research papers, and each student will deeply study the problem setting and methods described in one existing paper under our supervision, and report the final outcome in terms of open presentations followed with a Q&A session and reports. There will be no additional written or oral exam.

After attending the seminar, we expect the participants should have a good overview of current approaches to tackle 3D shape matching problems, and a deeper understanding about one particular method, which should lay a good foundation of future hands-on research projects.


All participants should have a solid working knowledge of linear algebra and calculus. In addition, it is useful (but not required), that students have a background in at least one of the following topics: continuous/discrete optimisation, 3D geometry, computer vision, image processing, or computer graphics.



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