Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality

PubDate: Apri 2024

Teams: HKUST(GZ) ；HKUST；ARC Lab, Tencent

Writers: Zidong Cao, Zhan Wang, Yexin Liu, Yan-Pei Cao, Ying Shan, Wei Zeng, Lin Wang

PDF: Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality

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 effectiveness of our system, we first evaluate our algorithm with state-of-the-art methods on public datasets, which achieves the best performance. Furthermore, we undertake a comprehensive user study to evaluate viewer experiences across diverse scenarios and to gather their qualitative feedback from multiple perspectives. The outcomes reveal that our system enhances user engagement by improving the viewers’ recognition, reducing discomfort, and improving the overall immersive experience. Our system makes the navigation and zoom more user-friendly.