Augmented reality (AR) is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. Enter AR technology, which can be used to visualize data from hundreds of sensors (Kinect, HoloLens, Intel Real Sense, and so on) simultaneously, overlaying relevant and actionable information over your environment through a headset. However, most augmented reality experiences today revolve around overlaying the physical world with known information. Maps and games have garnered much attention in the consumer tech space. In the practical applications, the AR capabilities being leveraged would be constituted as visualize, instruct, or guide. Some examples: Virtual work instructions for operating manuals, Service maintenance timely imprint digitized information in the real-world and in-context to the task at hand.
Artificial intelligence – and especially deep learning – ushers in a new wave of innovation to computer vision (CV) and augmented reality (AR). The ability to perceive an array of environments will unlock the next-generation of augmented reality use cases and further empower the front-line worker like never before. Understanding the differences between classical (or traditional) and learning computer vision is fundamental to developing applications today and in the near future. Industrial environments are extremely complex. Augmented reality technology based on vision is not only an effective data visualization technology, but also can train workers for operating machines effectively.
This essentially is the ‘design-your-own’ CV algorithm in a design and coding environment. An engineer can map native sensor inputs to 3D geometries and enable the CV algorithm to be recognized for a specific use case. The AR engineer or experience creator can bring this CV algorithm and specific use case to life in a 3D design authoring environment by aligning these geometries, points, features, and measurements to activate it in context. Computer vision and more specifically deep learning-based approaches embedded in the augmented reality application enabled this automatic object recognition.
In summary, the convergence of computer vision and augmented reality is a really cool upcoming wave. As a result, this special session aims to bring the latest results over computer vision for augmented reality. It can help technicians to exchange the latest technical progresses.
Topics include, but are not limited to:
Camera tracking for augmented reality
Deep learning for computer vision
3D object reconstruction in augmented reality
3D object recognition
3D object tracking in augmented reality
Color consistency in augmented reality
Color transfer in augmented reality
Communication between augmented reality devices
Real-world Applications of augmented reality: security; healthcare; and advertising
Paper Submission: September 15 2020
First round of reviews: December 15 2020
Submission of revised papers: February 15 2021
Second round of reviews: April 15 2021
Final Papers Submission: May 15, 2021
Dr Zhihan Lv (lead)
Professor Qingdao University, China.
Email: firstname.lastname@example.org, email@example.com
Professor, Polytechnic University of Valencia, Spain.
Assistant Professor, Embry-Riddle Aeronautical University, USA.
Email: firstname.lastname@example.org, SONGH4@erau.edu