Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality
Zidong Cao, Zhan Wang, Yexin Liu, Yan-Pei Cao, Ying Shan, Wei Zeng and, Lin Wang

TL;DR
This paper introduces OmniVR, a system that enhances visual clarity and user engagement in VR navigation of omnidirectional images by reducing blur through a learning-based refinement algorithm that responds to user commands.
Contribution
The paper presents a novel VR system, OmniVR, that improves image clarity during navigation and zooming in omnidirectional images using a learning-based algorithm responsive to user inputs.
Findings
OmniVR outperforms state-of-the-art methods on public datasets.
User studies show increased engagement and reduced discomfort.
The system makes VR navigation more intuitive and immersive.
Abstract
Viewing omnidirectional images (ODIs) in virtual reality (VR) represents a novel form of media that provides immersive experiences for users to navigate and interact with digital content. Nonetheless, this sense of immersion can be greatly compromised by a blur effect that masks details and hampers the user's ability to engage with objects of interest. In this paper, we present a novel system, called OmniVR, designed to enhance visual clarity during VR navigation. Our system enables users to effortlessly locate and zoom in on the objects of interest in VR. It captures user commands for navigation and zoom, converting these inputs into parameters for the Mobius transformation matrix. Leveraging these parameters, the ODI is refined using a learning-based algorithm. The resultant ODI is presented within the VR media, effectively reducing blur and increasing user engagement. To verify the…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques
