Visual odometry and localization maintain an increasing interest in recent years, especially with the extensive applications on autonomous driving, augmented reality, and mobile computing. With the location information obtained through odometry, services based on location clues are also rapidly emerging. Particularly, in this workshop, we focus on mobile platform applications. This workshop invites papers in the areas including advances in visual odometry and computer vision applications based on location context in topics listed below, but not limited:

• Mobile AR system

• Vision for Field Robotics

• AR glass applications

• 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 object and/or 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

Important Dates:



Publication in IEEE Xplore requires significant lead time. To guarantee your paper to appear on all these places (IEEE Xplore, CVPR proceedings, CVF), please follow the timelines below:



Paper Submission deadline: April 8th, 2022

Supplementary materials submission deadline: April 8th, 2022

Decision release deadline: April 16th, 2022

Camera ready submission deadline: April 20th, 2022



Submission Procedures:



Authors should follow CVPR 2022 Author Guidelines to prepare their manuscript. All papers should limit the main content to 8 pages. References are allowed to be listed in additional pages.

Conference website is at https://sites.google.com/view/vocvalc2022/home

Papers should be submitted through VOCVALC CMT website:

https://cmt3.research.microsoft.com/VOCVALC2022

For any problem, please contact Dr. Guoyu Lu guoyulu62@gmail.com