International Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues


Call for papers

With the advent of autonomous driving and augmented reality, the applications of visual odometry are significantly growing. The development of smart-phones and cameras is also making the visual odometry more accessible to common users in daily life. With the increasing efforts devoted to accurately computing the position information, emerging applications based on location context, such as scene understanding, city navigation and tourist recommendation, have gained significant growth. The location information can bring a rich context to facilitate a large number of challenging problems, such as landmark and traffic sign recognition under various weather and light conditions, and computer vision applications on entertainment based on location information, such as Pokemon. The motivation for the proposed workshop is soliciting scalable algorithms and systems for addressing the ever increasing demand of accurate and real-time visual odometry, as well as the methods and applications based on the location clues. This workshop invites papers in the areas including advances in visual odometry and its applications related to computer vision in topics listed below, but not limited:

  • Image-based localization and navigation
  • Monocular and stereo visual odometry
  • Visual odometry applications on autonomous driving
  • Augmented reality based on visual odometry
  • Robust pose estimation solutions
  • Multi-model visual sensor data fusion
  • Real-time object tracking
  • 3D scene modeling
  • Application of deep learning on visual odometry
  • Large-scale SLAM
  • Map generation
  • Scene understanding and semantic labeling
  • Rendering and visualization of large-scale models
  • Feature representation, indexing, storage and analysis
  • Feature extraction and matching
  • Object detection and recognition based on location context
  • Landmark mining and tourism recommendation
  • Video surveillance
  • Benchmark datasets collection

Organizers/Program chairs:

Guoyu Lu, Ford Research and Advanced Engineering
Yan Yan, University of Michigan
Friedrich Fraundorfer, Graz University of Technology
Nicu Sebe, University of Trento
Chandra Kambhamettu, University of Delaware

Program committee:

Will Maddern, Oxford University
Davide Scaramuzza, University of Zurich
Riad Hammoud, BAE Systems
Andreas Geiger, MPI
Gang Hua, Microsoft Research
Rudolf Mester, Goethe University
Xin Chen, HERE
Daniel Cremers, Technical University of Munich
Hideo Saito, Keio University
Vincent Lepetit, Graz University of Technology
Kurt Konolige, Google
Larry Matthies, Nasa Jet Propulsion Lab
Subramanian Ramamoorthy University of Edinburgh
Jeff Delaune, Nasa Jet Propulsion Lab
Srikumar Ramalingam, University of Utah
Andrew Richardson, Ford Research
Cornelia Fermuller, University of Maryland
Yasuyuki Matsushita, Osaka University
Adrien Bartoli, University of Auvergne
Salzmann Mathieu, EPFL
Manmohan Chandraker, NEC Labs
Sebastian Scherer, CMU
Rafael Valencia-Carreno, CMU
Tuomas Haarnoja, UC Berkeley
Saurabh Gupta, UC Berkeley
Fereshteh Sadeghi, University of Washington