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Visualizing the Invisible: An Efficient Framework for Microscopic Visualization
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AI
DOI：10.1007/978-981-95-0100-7_10.png)
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Abstract
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The growing focus on microscopic entities such as cells or viruses in medical education and public health has highlighted the need for better visualizations of microscopic life. Traditional methods like microscopy provide detailed images but are often hard for non-specialists to understand. Existing 3D models for visualization are mostly manually created and face issues with accuracy, time, and cost, limiting their applicability in large-scale educational and outreach efforts. This project presents an efficient and high-quality visualization framework that directly generates high-fidelity 3D models from real biological scanning data. The proposed approach integrates 3D reconstruction, texture mapping, coloring, and lighting, enabling the creation of detailed and accurate microscopic models with reduced labor and time costs. By overcoming the limitations of existing methods, this framework has the potential to enhance both medical education and public engagement with the microscopic world, offering an efficient, scalable, and accurate solution for visualizing complex biological structures.
Keywords:
Virus Visualization
Scientific Illustration
Volumetric Rendering
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