Symmetry is an essential property of a shapes’ appearance and presents a source
of information for structure-aware deformation and model synthesis. This thesis
proposes feature-based methods to detect symmetry and regularity in 3D shapes
and demonstrates the utilization of symmetry information for content generation.
First, we will introduce two novel feature detection techniques that extract salient
keypoints and feature lines for a 3D shape respectively. Further, we will propose
a randomized, feature-based approach to detect symmetries and decompose the
shape into recurring building blocks. Then, we will present the concept of docking
sites that allows us to derive a set of shape operations from an exemplar and will
produce similar shapes. This is a key insight of this thesis and opens up a new
perspective on inverse procedural modeling. Finally, we will present an interactive,
structure-aware deformation technique based entirely on regular patterns.