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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Computing 2D constrained delaunay triangulation using the GPU.

Meng Qi1, Thanh-Tung Cao, Tiow-Seng Tan

  • 1National University of Singapore, Singapore. qimeng@comp.nus.edu.sg

IEEE Transactions on Visualization and Computer Graphics
|March 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces the first graphics processing unit (GPU) solution for 2D constrained Delaunay triangulation (CDT) of planar straight line graphs (PSLGs). The novel GPU approach accelerates the entire computation, achieving significant speedups over CPU methods for complex geometric data.

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Area of Science:

  • Computational Geometry
  • Computer Graphics
  • Parallel Computing

Background:

  • Constrained Delaunay Triangulation (CDT) is crucial for various applications.
  • Existing algorithms primarily rely on Central Processing Units (CPUs).
  • Efficient GPU solutions for CDT of Planar Straight Line Graphs (PSLGs) are lacking.

Purpose of the Study:

  • To develop the first comprehensive graphics processing unit (GPU) solution for 2D constrained Delaunay triangulation (CDT).
  • To accelerate the computation of CDT for planar straight line graphs (PSLGs) using parallel GPU architecture.
  • To provide a robust and efficient alternative to existing CPU-bound algorithms.

Main Methods:

  • Developed a novel algorithm leveraging the parallel processing capabilities of GPUs.
  • Implemented the solution using the CUDA programming model on NVIDIA GPUs.
  • Tested the implementation on randomly generated PSLGs and real-world Geographic Information System (GIS) data.

Main Results:

  • The proposed GPU solution achieves up to an order of magnitude speedup compared to the best sequential CPU implementations.
  • The implementation demonstrates numerical robustness.
  • The method efficiently handles large-scale datasets with millions of points and edges.

Conclusions:

  • The GPU-based CDT computation is a significant advancement in computational geometry.
  • This approach offers substantial performance improvements for processing complex geometric data.
  • The method is suitable for large-scale applications, including those in GIS.