Dichoptic Opacity: Managing Occlusion in Stereoscopic Displays via Dichoptic Presentation
June 28, 2025 Β· Declared Dead Β· π Visual ..
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
George Bell, Alma Cantu
arXiv ID
2506.22841
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
Visual ..
Last Checked
5 months ago
Abstract
Adjusting transparency is a common method of mitigating occlusion but is often detrimental for understanding the relative depth relationships between objects as well as removes potentially important information from the occluding object. We propose using dichoptic opacity, a novel method for occlusion management that contrasts the transparency of occluders presented to each eye. This allows for better simultaneous understanding of both occluder and occluded. A user study highlights the technique's potential, showing strong user engagement and a clear preference for dichoptic opacity over traditional presentations. While it does not determine optimal transparency values, it reveals promising trends in both percentage and range that merit further investigation.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
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