Globally and Locally Optimized Pannini Projection for High FoV Rendering of 360-degree Images
June 05, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Falah Jabar, Joao Ascenso, Maria Paula Queluz
arXiv ID
2406.03282
Category
cs.MM: Multimedia
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
To render a spherical (360 degree or omnidirectional) image on planar displays, a 2D image -- called as viewport -- must be obtained by projecting a sphere region on a plane, according to the users viewing direction and a predefined field of view (FoV). However, any sphere to plan projection introduces geometric distortions, such as object stretching and/or bending of straight lines, which intensity increases with the considered FoV. In this paper, a fully automatic content-aware projection is proposed, aiming to reduce the geometric distortions when high FoVs are used. This new projection is based on the Pannini projection, whose parameters are firstly globally optimized according to the image content, followed by a local conformality improvement of relevant viewport objects. A crowdsourcing subjective test showed that the proposed projection is the most preferred solution among the considered state-of-the-art sphere to plan projections, producing viewports with a more pleasant visual quality.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
π
π
Old Age
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted