Data driven Methods for 3D Shape Analysis

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.