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A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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Efficient Ray Tracing of Large 3D Scenes for Mobile Distributed Computing Environments.

Woong Seo1, Sanghun Park2, Insung Ihm1

  • 1Department of Computer Science and Engineering, Sogang University, Seoul 04107, Korea.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

Researchers created a mobile ray tracer using smartphones for large dataset rendering. This mobile cluster system achieves interactive frame rates, enabling augmented reality applications.

Keywords:
augmented and mixed realitymaster/worker systemmobile GPU ray tracingmobile distributed computingspace-efficient 3D scene representation

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Area of Science:

  • Computer Graphics
  • Distributed Systems
  • Mobile Computing

Background:

  • Cluster computing is effective for large-scale problems.
  • Mobile computing clusters using smartphones offer a novel approach.
  • Limited research exists on mobile cluster-based rendering.

Purpose of the Study:

  • Investigate mobile cluster-based rendering of large datasets.
  • Develop a mobile GPU ray tracer for 3D scenes.
  • Explore feasibility for augmented and mixed reality applications.

Main Methods:

  • Developed a mobile GPU ray tracer for commodity smartphones.
  • Implemented an effective 3D scene representation for mobile GPU rendering.
  • Utilized a static load balancing strategy for mobile networks.

Main Results:

  • Rendered 3D scenes with millions of triangles at interactive frame rates.
  • Achieved a few frames per second on 16 smartphones for large scenes.
  • Demonstrated potential for augmenting real-world scenes with ray-traced images.

Conclusions:

  • Mobile clusters are viable for rendering large datasets.
  • Static load balancing is suitable for mobile clustering.
  • The system can enhance augmented and mixed reality experiences.