April 23 – 28 , 2017, Dagstuhl Seminar 17171

Computational Geometry


Otfried Cheong (KAIST – Daejeon, KR)
Anne Driemel (TU Eindhoven, NL)
Jeff Erickson (University of Illinois – Urbana-Champaign, US)

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Dagstuhl Report, Volume 7, Issue 4 Dagstuhl Report
List of Participants
Shared Documents
Dagstuhl Seminar Schedule [pdf]


Computational geometry is concerned with the design, analysis, and implementation of algorithms for geometric and topological problems, which arise naturally in a wide range of areas, including computer graphics, robotics, geographic information systems, molecular biology, sensor networks, machine learning, data mining, scientific computing, theoretical computer science, and pure mathematics. Computational geometry is a vibrant and mature field of research, with several dedicated international conferences and journals, significant real-world impact, and strong intellectual connections with other computing and mathematics disciplines.

Seminar Topics. The emphasis of the seminar will be on presenting recent developments in computational geometry, as well as identifying new challenges, opportunities, and connections to other fields of computing. In addition to the usual broad coverage of emerging results in the field, the seminar will include invited survey talks on two broad and overlapping focus areas that cover a wide range of both theoretical and practical issues in geometric computing. Both focus areas have seen exciting recent progress and offer numerous opportunities for further cross-disciplinary impact.

Computational geometry for monitoring and shape data. The combination of movement and geometry has always been an important topic in computational geometry, initially motivated by robotics and resulting in the study of kinetic data structures. With the advent of widely available location tracking technologies such as GPS sensors, trajectory analysis has become a topic in itself, which has connections to other classical topics in computational geometry such as shape analysis. Still, efficient technologies to perform the most basic operations are lacking. We need data structures supporting similarity queries on trajectory data and geometric clustering algorithms that can handle the infinite-dimensional geometry inherent in the data. A related type of data, namely time series data, has not received much attention in the computational geometry community, despite its universality and its close relation to trajectory data. Shedding light on the interconnections of these topics will promote new results in the field which will address these timely questions.

Computing in high-dimensional and infinite-dimensional spaces. The famous “curse of dimensionality” prevents exact geometric computations in high-dimensional spaces. Most of the data in science and engineering is high-dimensional, rendering classical geometric techniques, such as the sweepline approach, insufficient. One way to address this issue is to use sparsity, but it is not always easy to find a sparse representation of the data. The search of the most efficient representation and how to exploit this representation leads to dimension-reduction techniques, metric embeddings, and approximation algorithms. This line of research has strong ties to machine learning and discrete mathematics as well as computational geometry.

  Creative Commons BY 3.0 DE
  Otfried Cheong, Anne Driemel, and Jeff Erickson

Dagstuhl Seminar Series


  • Data Structures / Algorithms / Complexity


  • Combinatorics
  • Complexity
  • Algorithms
  • Geometric computing
  • Implementation
  • Applications
  • Monitoring and shape data
  • High-dimensional computational geometry

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