Welcome to the Computer Graphics Group at University of Bern!

Best paper award SGP 2022

The paper TinyAD: Automatic Differentiation in Geometry Processing Made Simple, co-authored by Prof. David Bommes, won the Best Paper Award 1st place at the Symposium on Geometry Processing 2022. SGP2022/awards

July 7, 2022

Invited talk by visiting researcher Hendrik Brückler, Osnabrück University, Germany

Title: "How to build hex meshes using motorcycles (not the other way around)” Monday, June 13th, 2022, Time: 11:00 Location: N10, room 104

June 13, 2022

Invited talk by visiting researcher Tim Felle Olsen, TU Denmark

Title: Synthesis of Frame Field-aligned Multi-Laminar Structures Thursday, October 28th, 2021, Time: 16:30 Location: N10, room 302

Oct. 28, 2021

Best paper award SGP 2021

The paper Geodesic Distance Computation via Virtual Source Propagation, coauthored by Prof. David Bommes, won the Best Paper 2nd Place at the Symposium on Geometry Processing 2021. SGP2021/awards

Sept. 10, 2021

Invited Talks from Hextreme team UCL, Belgium

Pierre-Alexandre Beaufort and Maxence Reberol (both from the hextreme team at UC Louvain) will be visiting us. There will be two talks on Wednesday 4th, 11:00 and 14:00 respectively, see Talks section to learn more.

March 4, 2020

Invited talk, Max Lyon. RWTH Aachen University

Title: Parametrization Quantization with Free Boundaries for Trimmed Quad Meshing Friday, Feb. 21st Time: 10:00 Location: N10, room 302

Feb. 18, 2020

Recent Publications

Hex-Mesh Generation and Processing: a Survey

ACM Transactions on Graphics 2022

In this article, we provide a detailed survey of techniques for hexahedral mesh generation. We cover the whole spectrum of alternative approaches to mesh generation, as well as post processing algorithms for connectivity editing and mesh optimization. For each technique, we highlight capabilities and limitations, also pointing out the associated unsolved challenges. Recent relaxed approaches, aiming to generate not pure-hex but hex-dominant meshes, are also discussed. The required background, pertaining to geometrical as well as combinatorial aspects, is introduced along the way.


Volume Parametrization Quantization for Hexahedral Meshing


Developments in the field of parametrization-based quad mesh generation on surfaces have been impactful over the past decade. In this context, an important advance has been the replacement of error-prone rounding in the generation of integer-grid maps, by robust quantization methods. In parallel, parametrization-based hex mesh generation for volumes has been advanced. In this volumetric context, however, the state-of-the-art still relies on fragile rounding, not rarely producing defective meshes, especially when targeting a coarse mesh resolution. We present a method to robustly quantize volume parametrizations, i.e., to determine guaranteed valid choices of integers for 3D integer-grid maps. Inspired by the 2D case, we base our construction on a non-conforming cell decomposition of the volume, a 3D analogue of a T-mesh. In particular, we leverage the motorcycle complex, a recent generalization of the motorcycle graph, for this purpose. Integer values are expressed in a differential manner on the edges of this complex, enabling the efficient formulation of the conditions required to strictly prevent forcing the map into degeneration. Applying our method in the context of hexahedral meshing, we demonstrate that hexahedral meshes can be generated with significantly improved flexibility.


Hex Me If You Can

Computer Graphics Forum (SGP 2022)

HexMe consists of 189 tetrahedral meshes with tagged features and a workflow to generate them. The primary purpose of HexMe meshes is to enable consistent and practically meaningful evaluation of hexahedral meshing algorithms and related techniques, specifically regarding the correct meshing of specified feature points, curves, and surfaces. The tetrahedral meshes have been generated with Gmsh, starting from 63 computer-aided design (CAD) models from various databases. To highlight and label the diverse and challenging aspects of hexahedral mesh generation, the CAD models are classified into three categories: simple, nasty, and industrial. For each CAD model, we provide three kinds of tetrahedral meshes (uniform, curvature-adapted, and box-embedded). The mesh generation pipeline is defined with the help of Snakemake, a modern workflow management system, which allows us to specify a fully automated, extensible, and sustainable workflow. It is possible to download the whole dataset or select individual meshes by browsing the online catalog. The HexMe dataset is built with evolution in mind and prepared for future developments. A public GitHub repository hosts the HexMe workflow, where external contributions and future releases are possible and encouraged. We demonstrate the value of HexMe by exploring the robustness limitations of state-of-the-art frame-field-based hexahedral meshing algorithm. Only for 19 of 189 tagged tetrahedral inputs all feature entities are meshed correctly, while the average success rates are 70.9% / 48.5% / 34.6 % for feature points/curves/surfaces.