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

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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, Rochester Institute of Technology
Friedrich Fraundorfer, Graz University of Technology
Yan Yan, Texas State University
Nicu Sebe, University of Trento
Chandra Kambhamettu, University of Delaware


Program committee:

Rudolf Mester, Linköping University
John Drinkard, Omron Research Center
Manmoham Chandraker, NEC Labs
Adrien Bartoli, University of Auvergne
Riad Hammoud, BAE Systems
Carl Salvaggio, Rochester Institute of Technology
Quoc-Huy Tran, NEC Labs
Will Maddern, Oxford University
Cornelia Fermuller, University of Maryland
Vincent Lepetit, Graz University of Technology
Daniel Kaputa, Rochester Institute of Technology
Jeff Delaune, NASA Jet Propulsion Lab
Pan Ji, NEC Labs
Xin Chen, HERE Maps
Huili Yu, Omron Research Center
Dong Zhang, Nvidia
Sebastian Scherer, CMU
Davide Scaramuzza, University of Zurich
Christopher Kanan , Rochester Institute of Technology
Anelia Angelova, Google Brain
Hongdong Li, Australian National University
Hui Zhou, JD.com
Keith Sullivan, Naval Research Lab
John Galeotti, CMU
Kurt Konolige, Google X
Manoranjan Majji, Texas A&M University
Andreas Savakis, Rochester Institute of Technology
Jiangping Wang, Siemens Corporate Research
David Held, CMU
Feras Dayoub, Queensland University of Technology
Ioannis Gkioulekas, CMU
Andreas Geiger, MPI
Peidong Liu, ETH