GPU-based framework for 3D Delaunay triangulation using the gFlip3D parallel insertion algorithm has been presented here. Traditional CPU implementations of Delaunay triangulation are computationally intensive and do not scale well for large point clouds. Making use of the massive parallelism of modern GPUs, gFlip3D gets up to 24× speedup over CPU baselines and keeps throughputs from 4 to 5 million points per second on datasets larger than 100 million points. The approach offers finalization of space for memory efficiency and also has the capability to output partial streams, allowing real-time rendering of triangulated geometry. The empirical evidence demonstrates a quasi-linear scalability concerning the execution time in conjunction with good frame rates (≥1 fps) for datasets up to 1 million points. Performance validated over years of strong experimentation over synthetic and real-world data. Results established gFlip3D as an extremely scalable, high-performance solution for graphics-intensive applications such as interactive visualization, AR/VR systems, and scientific modeling.

GPU, Delaunay Triangulations, Insertion Strategies, Scalability Test, Memory Footprint