  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.
