Shape Analysis and 3D Deep Learning


SS 2023





ECTS 6 (V3/Ü2)





Former: "Data driven Methods for 3D Shape Analysis"

Please note that the lecture starts Tuesday April 18

The first exercise will be held Wednesday April 12


Knowledge: After successful completion of the module, students know

  • different representations for 3D shapes (e.g. meshes, voxel grids, point clouds, implicit functions)
  • how to analyse single shapes (e.g. feature descriptors, global/partial symmetries, segmentations, decompositions)
  • 3D deep learning (e.g. suitable representations, shape encoding, decoding and generation, learning high-level structures)

Skills: After successful completion of the module, students will be able to

  • explain algorithms and methods for analyzing and generating 3D geometry
  • understand geometry related aspects and challenges in the domain of deep learning
  • implement neural networks for basic geometry related tasks

Competencies: Based on the knowledge and skills acquired in the module, students will be able to

  • discuss how shape analysis can be used for high- and low-level geometry processing tasks
  • make an informed choice in how to represent 3D shapes for different tasks
  • make an informed choice in opting for a model-driven, a data-driven, or a hybrid approach, depending on the data availability
Starting with SS 2023 we will restructure the lecture and focus more on 3D Deep Learning

The lecture covers the following topics:

  • Data Analysis Fundamentals

    • Dimensionality Reduction
    • Clustering
  • Classical Shape Analysis

    • Shape Representations
    • Global and Local Shape Descriptors
    • Shape Distance Measures and Structures
  • 3D Deep Learning

    • Shape Encoding and Decoding

      • Images
      • Neural Radiance Fields
      • Point Clouds
      • Voxels
      • Implicit Functions
      • Graphs and Manifolds
    • Methods for 3D Shape Generation

      • Vector Quantized Variational Autoencoders
      • Autoregressive Transformers
      • Generative Adversarial Networks
      • Diffusion Models


  • Weekly exercises (mandatory practical, optional theoretical)
  • Practical part in Python
  • 120 minutes exam
  • Exam admittance requires 50% of practical exercise points
  • Small exam bonus for 75% of practical exercise points


  • Basic understanding of Neural Networks is recommended, but we will provide an "Introduction to Deep Learning"
  • The lecture "Basic Techniques in Computer Graphics" and "Geometry Processing" is considered helpful, but not a hard requirement
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