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Data driven Methods for 3D Shape Analysis


Semester:

SS 2022

Type:

Lecture

Lecturer:

Credits:

ECTS 6 (V3/Ü2)

Contact:

shapeanalysis@cs.rwth-aachen.de

Contents

The lecture covers the following topics:

  • Clustering
  • Dimensionality Reduction
  • Global and Local Shape Descriptors
  • Shape Structures
  • Distance Measures
  • Surface Maps
  • Learning based approaches for Shape Encoding
  • Learning based approaches for Shape Decoding
  • Generation of novel Shapes

Organizational

  • 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

Prerequisites

  • The lecture "Basic Techniques in Computer Graphics" is recommended but not a hard requirement
  • The lecture "Geometry Processing" is considered helpful, but also not required.

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