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.

翻译：本文提出ScaleFree，一种基于GPU加速的自适应核密度估计（KDE）算法，用于实现可扩展的交互式多尺度点云探索。该技术面向先进科学计算中的海量数据集与复杂多尺度结构，例如包含数十亿粒子的宇宙学模拟。对此类数据进行有效探索需要完整的三维空间结构理解能力，而沉浸式环境（如VR）在此方面具有独特优势。然而，如何同时支持全局多尺度上下文与细粒度局部细节仍存在重大挑战。核心难点在于如何从点云动态生成连续密度场以实现无缝尺度过渡：尽管KDE被广泛使用，但预计算场会限制交互精度并忽略细微结构，而动态计算的计算成本通常难以满足实时VR交互需求。我们通过结合GPU加速、基于k-d树的空间查询以及线程组内的并行归约技术，实现了实时密度估计。该方法支持根据用户在跨尺度探索中的关注焦点动态重计算标量场。我们通过自适应选取与渐进式导航两项数据探索任务，展示了自适应密度估计的优势。性能实验表明，基于GPU并行实现的ScaleFree相较串行及多核CPU基线实现数量级加速。在受控实验中，我们进一步验证了所提自适应选取技术在多尺度选取任务中提升了准确性与效率。