ScaleFree: Dynamic KDE for Multiscale Point Cloud Exploration in VR
Lixiang Zhao, Fuqi Xie, Tobias Isenberg, Hai-Ning Liang, Lingyun Yu
TL;DR
ScaleFree addresses the challenge of exploring massive, multiscale point clouds in VR by introducing a GPU-accelerated adaptive KDE that recomputes density fields on demand as users change scale. The method decomposes KDE into pilot density computation, adaptive smoothing length updates, and final density estimation, all implemented on GPU with a k-d tree to accelerate neighborhood queries. In experiments and a user study, ScaleFree achieved orders-of-magnitude speedups over CPU baselines and improved accuracy, efficiency, and user workload in multiscale navigation and selection tasks compared to precomputed density-field approaches. The work enables a new interaction paradigm for immersive analytics, where density-driven ROI selection and progressive navigation can fluidly traverse scales with minimal perceptual delay, making large-scale scientific visualization in VR more practical and informative.
Abstract
We present ScaleFree, a GPU-accelerated adaptive Kernel Density Estimation (KDE) algorithm for scalable, interactive multiscale point cloud exploration. With this technique, we cater to the massive datasets and complex multiscale structures in advanced scientific computing, such as cosmological simulations with billions of particles. Effective exploration of such data requires a full 3D understanding of spatial structures, a capability for which immersive environments such as VR are particularly well suited. However, simultaneously supporting global multiscale context and fine-grained local detail remains a significant challenge. A key difficulty lies in dynamically generating continuous density fields from point clouds to facilitate the seamless scale transitions: while KDE is widely used, precomputed fields restrict the accuracy of interaction and omit fine-scale structures, while dynamic computation is often too costly for real-time VR interaction. We address this challenge by leveraging GPU acceleration with k-d-tree-based spatial queries and parallel reduction within a thread group for on-the-fly density estimation. With this approach, we can recalculate scalar fields dynamically as users shift their focus across scales. We demonstrate the benefits of adaptive density estimation through two data exploration tasks: adaptive selection and progressive navigation. Through performance experiments, we demonstrate that ScaleFree with GPU-parallel implementation achieves orders-of-magnitude speedups over sequential and multi-core CPU baselines. In a controlled experiment, we further confirm that our adaptive selection technique improves accuracy and efficiency in multiscale selection tasks.