We propose three novel spatial data selection techniques for particle data in VR visualization environments. They are designed to be target- and context-aware and be suitable for a wide range of data features and complex scenarios. Each technique is designed to be adjusted to particular selection intents: the selection of consecutive dense regions, the selection of filament-like structures, and the selection of clusters -- with all of them facilitating post-selection threshold adjustment. These techniques allow users to precisely select those regions of space for further exploration -- with simple and approximate 3D pointing, brushing, or drawing input -- using flexible point- or path-based input and without being limited by 3D occlusions, non-homogeneous feature density, or complex data shapes. These new techniques are evaluated in a controlled experiment and compared with the Baseline method, a region-based 3D painting selection. Our results indicate that our techniques are effective in handling a wide range of scenarios and allow users to select data based on their comprehension of crucial features. Furthermore, we analyze the attributes, requirements, and strategies of our spatial selection methods and compare them with existing state-of-the-art selection methods to handle diverse data features and situations. Based on this analysis we provide guidelines for choosing the most suitable 3D spatial selection techniques based on the interaction environment, the given data characteristics, or the need for interactive post-selection threshold adjustment.

翻译：我们提出了三种适用于VR可视化环境中粒子数据的空间选择技术。这些技术具备目标与上下文感知能力，能适应多样化的数据特征与复杂场景。每种技术针对特定选择意图进行设计：连续密集区域选择、纤维状结构选择以及簇状结构选择——所有技术均支持选择后阈值调整。用户可通过简单的近似3D指向、刷选或绘制输入，结合灵活的基于点或路径的交互方式，精准选择空间区域进行后续探索，且不受3D遮挡、非均匀特征密度或复杂数据形状的限制。通过控制实验将新技术与基准方法（基于区域的3D绘画选择）进行比较。结果表明，我们的技术能有效处理广泛场景，并支持用户基于对关键特征的理解进行数据选择。此外，我们分析了空间选择方法的属性、需求与策略，并将其与现有先进选择方法进行对比，以应对多样化的数据特征与情境。基于此分析，我们提供了根据交互环境、给定数据特征或交互式后选择阈值调整需求选择最合适3D空间选择技术的指导准则。