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Welcome to the Computer Graphics Group at University of Bern!

New SIGGRAPH ASIA paper

The paper A Progressive Embedding Approach to Bijective Tetrahedral Maps driven by Cluster Mesh Topology was accepted for publication and will be presented at SIGGRAPH ASIA 2024 Conference

Sept. 18, 2024

New SIGGRAPH papers

The three papers Expansion Cones: A Progressive Volumetric Mapping Framework, Locally Meshable Frame Fields and Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization were accepted for publication and are to be presented in SIGGRAPH 2023 Conference

May 12, 2023

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

Recent Publications

A Progressive Embedding Approach to Bijective Tetrahedral Maps driven by Cluster Mesh Topology

SIGGRAPH ASIA 2024

We present a novel algorithm to map ball-topology tetrahedral meshes onto star-shaped domains with guarantees regarding bijectivity. Our algorithm is based on the recently introduced idea of Shrink-and-Expand, where images of interior vertices are initially clustered at one point (Shrink-), before being sequentially moved to non-degenerate positions yielding a bijective map (-and-Expand). In this context, we introduce the concept of the cluster mesh, i.e. the unexpanded interior mesh consisting of geometrically degenerate simplices. Using local, per-vertex connectivity information solely from the cluster mesh, we show that a viable expansion sequence guaranteed to produce a bijective map can always be found as long as the mesh is shellable. In addition to robustness guarantees for this ubiquitous class of inputs, other practically relevant benefits include improved parsimony and reduced algorithmic complexity. While inheriting some of the worst-case high run time requirements of the state of the art, significant acceleration for the average case is experimentally demonstrated.

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