Praktikum: 3D Scanning & Spatial Learning (IN2106, IN4263)
Course 0000004813 in SS 2023
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 28 - Associate Professorship of Visual Computing (Prof. Nießner) |
| Lecturers |
Simon Giebenhain Tobias Kirschstein Matthias Nießner Susanne Weitz |
| Dates |
Thu, 10:00–12:00, MI 02.09.023 Thu, 10:00–12:00, MI 02.09.023 and 1 singular or moved dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | 3D scanning and motion capture are of paramount importance for content creation, man-machine and machine-environment interaction. In this course, we will continue the topics covered by the 3D Scanning & Motion Capture as well as by the Introduction to Deep Learning lecture. In the spirit of ‘learning by doing’, the students are asked to implement state-of-the-art reconstruction methods or current research topics in the field. Specifically, we will have projects on: ● Real-time facial motion capturing (sparse and dense approaches) ● Photorealistic Rendering of Avatars (e.g., Mixture of Volumetric Primitives) ● Intuitive Face Animation (e.g., via user strokes / DeepSpeech audio conditioning) ● High-quality Geometry Reconstruction from Videos (e.g., via nvdiffrast / NeuS) ● Hair Reconstruction (e.g., via line-based multi-view stereo) ● Neural Rendering of 3D content (e.g., DeepVoxel, NeuralRendering) |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |