Ascribe New Dimensions to Scientific Data Visualization with VR
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Abstract (arXiv)
For over half a century, the computer mouse has been the primary tool for interacting with digital data, yet it remains a limiting factor in exploring complex, multi-scale scientific images. Traditional 2D visualization methods hinder intuitive analysis of inherently 3D structures. Virtual Reality (VR) offers a transformative alternative, providing immersive, interactive environments that enhance data comprehension. This article introduces ASCRIBE-VR, a VR platform of Autonomous Solutions for Computational Research with Immersive Browsing & Exploration, which integrates AI-driven algorithms with scientific images. ASCRIBE-VR enables multimodal analysis, structural assessments, and immersive visualization, supporting scientific visualization of advanced datasets such as X-ray CT, Magnetic Resonance, and synthetic 3D imaging. Our VR tools, compatible with Meta Quest, can consume the output of our AI-based segmentation and iterative feedback processes to enable seamless exploration of large-scale 3D images. By merging AI-generated results with VR visualization, ASCRIBE-VR enhances scientific discovery, bridging the gap between computational analysis and human intuition in materials research, connecting human-in-the-loop with digital twins.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 27 references that are in the map. First posted on arXiv 18 Apr 2025.
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References · 4
- 2025A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Imagescited by 8
- 2024An Ensemble Approach for Brain Tumor Segmentation and Synthesiscited by 5
- 2017Portable learning environments for hands-on computational instruction: Using container- and cloud-based technology to teach data sciencecited by 3
- 2023Lithium Metal Battery Quality Control via Transformer-CNN Segmentationcited by 1
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