Scalable Comparative Visualization of Volumetric Data: Supporting 1-to-1, 1-to-Many, and Many-to-Many Comparisons

Description

Develop an interactive visualization framework that enables scalable comparison of 3D/4D datasets (e.g., medical imaging volumes, 3D simulations, point clouds, or scientific datasets---all potentially over time), allowing users to efficiently explore similarities, differences, and trends across single or multiple volumes without overwhelming the interface.

Key Challenges

• Visualizing multiple volumetric datasets simultaneously without occlusion.
• Supporting different comparison modes:
  • 1-to-1: Comparison of two instances.
  • 1-to-Many: Comparing a reference volume against multiple volumes.
  • Many-to-Many: Comparisons across a large collection of volumes.
• Maintaining interactivity and perceptual clarity while scaling.
• Efficient data management (GPU/CPU) for large volumetric datasets.
   

Tasks

• Review the state of the art in comparative visualization
• Design comparative layouts for 3D/4D data
• Potentially, develop interaction techniques
• Implement scalable data handling
• Evaluation (with a possible benchmark against existing 3D comparison approaches).

Requirements

• Visualization & computer graphics
• Experience with volumetric data formats (CT/MRI scans, CFD simulations, etc.)
• Familiarity with GPU acceleration or large data handling
• Creativity :) 

References

https://www.sciencedirect.com/science/article/pii/S0097849317300481