3 papers
cs.CV2026
Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction
Gregor Kobsik, Tim Elsner, Leif Kobbelt
Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding. Generative image models trained at scale have recent…
cs.CV2026
Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches
Gregor Kobsik, Isaak Lim, Leif Kobbelt
Detecting partial extrinsic symmetry in 3D geometry is a fundamental yet persistent challenge in computer vision and graphics, critical for tasks ranging from shape completion to p…
cs.CV2025
Learning Fine-to-Coarse Cuboid Shape Abstraction
Gregor Kobsik, Morten Henkel, Yanjiang He +4
The abstraction of 3D objects with simple geometric primitives like cuboids allows to infer structural information from complex geometry. It is important for 3D shape understanding…