FB20 Computer Science · 6 CP (estimated from SWS) · Offered in WiSe 2026/27, SoSe 2026, WiSe 2025/26
Average of averages
2.39
Every semester counts equally, whatever its size.
Pooled average
2.57
Weighted by cohort size — what an arbitrary student across all semesters scored.
Pass rate
82%
Share of results at 4.0 or better. 4.0 is the last passing grade.
Semesters with data
3
The German scale runs 1.0 (best) to 4.0 (last pass), with 5.0 as fail. There is no 4.3.
Missing (no reason): 1Reported Oct 15, 2026
Missing (medical excuse): 4Missing (no reason): 8Reported Sep 2, 2026
Missing (no reason): 2Reported Aug 31, 2026
100% positive of 1 rating
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The lecture will cover AI-based 3D reconstruction from various input modalities (Webcams, RGB-D cameras (Kinect, Realsense, …). The lecture builds upon the classical 3D reconstruction methods discussed in the ‘3D Scanning & Motion Capture’ lecture and shows how components and data structures of those methods can be replaced or extended by methods from AI. It will start with basic concepts of 2D neural rendering, including methods like Pix2Pix, Deferred Neural Rendering and alike. Then, more advanced topics like 3D/4D neural scene representations are discussed. To train those representations, differentiable rendering needs to be understood. The lecture will introduce methods for static and dynamic reconstruction with different levels of controllability and editability. Concept of 2D Neural Rendering Deferred Neural Rendering, AI-based Image-based Rendering. 3D Neural Rendering and Neural Scene Representations Neural Radiance Fields (NeRFs) Neural Point-based Graphics, Gaussian Splatting Differentiable Rendering (Rasterization, Volume Rendering, Shading) Relighting and Material Reconstruction DeepFakes Outlook: detection of synthetic media
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