Computer Science Graduate Seminar

Tuesday, February 11, 2020, 2:00pm

Feature-aware and feature-driven editing of 3D surface meshes

  • Location: Ahornstraße 55, E3, Room 118 (seminar room I8)
  • Speaker: Dipl.-Inform. Ellen Dekkers, Chair for Computer Science 8

 

Abstract

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 this talk, we review both research fields and discuss the respective approaches with a particular focus on their capabilities in preserving various types of surface features.

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.

Finally, we remove the manifold restriction and address 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.

 

The computer science lecturers invite interested people to join.