Feature-aware and feature-driven editing of 3D surface meshes
Dekkers, Ellen Sandra; Kobbelt, Leif (Thesis advisor); Bommes, David (Thesis advisor)
Aachen : RWTH Aachen University (2020, 2021)
Dissertation / PhD Thesis
In: Selected topics in computer graphics 19
Page(s)/Article-Nr.: 1 Online-Ressource (xi, 179 Seiten) : Illustrationen, Diagramme
Dissertation, RWTH Aachen University, 2020
Feature-aware and feature-driven editing of three-dimensional surface meshes is a very prominent task in Computer Graphics and Geometry Processing applications. Existing methods can be roughly classified into general elastic approaches, which aim at the preservation of a manifold surface's differential properties and hence of local, low-level geometric surface detail, and structure-aware editing techniques focusing on the preservation of high-level surface structures such as feature curves or regular patterns. In Part I of this thesis, we conduct a thorough review of the state of the art in both research fields and discuss the respective approaches with a particular focus on their capabilities in preserving various types of surface features. In Part II, we then present a novel approach to feature-aware mesh editing that combines elastic Laplacian deformation with discrete plastic topology modifications by transferring the concept of seam carving from the image retargeting to the mesh deformation scenario. During editing, a precomputed set of triangle strips, or geometry seams, can be dynamically deleted or inserted in low saliency mesh regions, thereby distributing the deformation distortion non-homogeneously over the model which yields a much better preservation of salient surface features compared to standard elastic deformation. Part III then removes the manifold restriction and addresses feature curve driven editing of non-manifold meshes. First, we propose a semi-automatic approach to efficiently and robustly recover characteristic feature curves from free-form surfaces. We then present two practical applications of this technique, the first of which exploits the curves' shape-defining properties and employs them as intuitive modeling handles for editing non-manifold surfaces. In our second application, we turn to a practical scenario in reverse engineering and consider the problem of generating a statistical shape model for car bodies. The crucial step of establishing proper feature correspondences between a large number of input models that exhibit significant shape variations is essentially guided by characteristic feature curves. These curves furthermore serve as modeling metaphors for intuitive exploration of the shape space spanned by the input models, thereby enabling the generation of semantically meaningful, novel car bodies.