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Data physicalizations (3D printed terrain models, anatomical scans, or even abstract data) can naturally engage both the visual and haptic senses in ways that are difficult or impossible to do with traditional planar touch screens and even immersive digital displays. Yet, the rigid 3D physicalizations produced with today's most common 3D printers are fundamentally limited for data exploration and querying tasks that require dynamic input (e.g., touch sensing) and output (e.g., animation), functions that are easily handled with digital displays. We introduce a novel style of hybrid virtual + physical visualization designed specifically to support interactive data exploration tasks. Working toward a "best of both worlds" solution, our approach fuses immersive AR, physical 3D data printouts, and touch sensing through the physicalization. We demonstrate that this solution can support three of the most common spatial data querying interactions used in scientific visualization (streamline seeding, dynamic cutting places, and world-in-miniature visualization). Finally, we present quantitative performance data and describe a first application to exploratory visualization of an actively studied supercomputer climate simulation data with feedback from domain scientists.
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Depth-assisted calibration on learning-based factorization for a compressive light field display
Due to the widespread applications of high-dimensional representations in many fields, the three-dimension (3D) display technique is increasingly being used for commercial purpose in a holographic-like and immersive demonstration. However, the visual discomfort and fatigue of 3D head mounts demonstrate the limits of usage in the sphere of marketing. The compressive light field (CLF) display is capable of providing binocular and motion parallaxes by stacking multiple liquid crystal screens without any extra accessories. It leverages optical viewpoint fusion to bring an immersive and visual-pleasing experience for viewers. Unfortunately, its practical application has been limited by processing complexity and reconstruction performance. In this paper, we propose a dual-guided learning-based factorization on polarization-based CLF display with depth-assisted calibration (DAC). This substantially improves the visual performance of factorization in real-time processing. In detail, we first take advantage of a dual-guided network structure under the constraints of reconstructed and viewing images. Additionally, by utilizing the proposed DAC, we distribute each pixel on displayed screens following the real depth. Furthermore, the subjective performance is increased by using a Gauss-distribution-based weighting (GDBW) toward the concentration of the observer’s angular position. Experimental results illustrate the improved performance in qualitative and quantitative aspects over other competitive methods. A CLF prototype is assembled to verify the practicality of our factorization.
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- Award ID(s):
- 1747751
- PAR ID:
- 10394522
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 31
- Issue:
- 4
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 5399
- Size(s):
- Article No. 5399
- Sponsoring Org:
- National Science Foundation
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