Computer Graphics and Visual Computing (CGVC) 2018

Analyses of large 3D particle datasets typically involve many different exploration and visualization steps. Interactive exploration techniques are essential to reveal and select interesting subsets like clusters or other sophisticated structures. State-of-the-art techniques allow for context-aware selections that can be refined dynamically. However, these techniques require large amounts of memory and have high computational complexity which heavily limits their applicability to large datasets. We propose a novel, massively parallel particle selection method that is easy to implement and has a processing complexity of O(n*k) (where n is the number of particles and k the maximum number of neighbors per particle) and requires only O(n) memory. Furthermore, our algorithm is designed for GPUs and performs a selection step in several milliseconds while still being able to achieve high-quality results.

CCS Concepts: Human-centered computing --> Visualization systems and tools; Interaction techniques; Scientific visualization

---

  full paper
<<< back