Point&Grasp probabilistically integrates pointing and grasp gestures for out-of-reach object selection in MR, trained on a new ORG dataset, and outperforms single-cue baselines in user studies.
In: IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
LandSAR integrates real-time landslide simulations, visualizations, and 3D-printed tangible terrain models to improve situational awareness and engagement for analysts.
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.
citing papers explorer
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Point & Grasp: Flexible Selection of Out-of-Reach Objects Through Probabilistic Cue Integration
Point&Grasp probabilistically integrates pointing and grasp gestures for out-of-reach object selection in MR, trained on a new ORG dataset, and outperforms single-cue baselines in user studies.
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LandSAR: Visceralizing Landslide Data for Enhanced Situational Awareness in Immersive Analytics
LandSAR integrates real-time landslide simulations, visualizations, and 3D-printed tangible terrain models to improve situational awareness and engagement for analysts.
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Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.