Particle Selection in Virtual Reality

@misc{kerengil2026particle,
  title = {{Particle Selection in Virtual Reality}},
  author = {Boaz Keren-Gil and James Gain and Lucia Marchetti and Patrick Marais and Alexander Sivitilli},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.17094},
  eprint = {2610.17094},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.17094},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - Particle Selection in Virtual Reality
AU  - Boaz Keren-Gil
AU  - James Gain
AU  - Lucia Marchetti
AU  - Patrick Marais
AU  - Alexander Sivitilli
PY  - 2026
DA  - 2026/10/12
PB  - arXiv
DO  - 10.48550/arxiv.2610.17094
UR  - https://arxiv.org/abs/2610.17094
AB  - Point-cloud representations of 3D data are used in many fields, from astronomical surveys of galaxies to medical imaging and laser scans of buildings. A task common to all of these is interactive selection: picking out a meaningful subset of points for closer analysis. The data is inherently 3D, yet the traditional workflow uses a 2D mouse on a 2D projection and inherits the limitations of a flat screen. This paper compares Virtual Reality (VR) and desktop environments for 3D particle selection, asking whether VR's six-degree-of-freedom hand interaction yields measurable gains in accuracy, speed, usability, workload and flow. In a counterbalanced within-subjects experiment (N = 34), participants selected 90 target galaxies from a catalogue of 16,816 galaxies, using mouse-driven selection with keyboard camera control on the desktop and a hand-held spherical brush that adds or removes particles in VR. We measured task accuracy, completion time, and three standard questionnaires: the System Usability Scale (SUS), the NASA Task Load Index (NASA-TLX) and the Flow State Scale (FSS). Accuracy was comparable (91.6% VR vs. 93.1% desktop; p = 0.858), but every other measure favoured VR. Tasks were 23% faster (p = 0.001, d = 0.93), usability was higher (SUS 84.6 vs. 54.0; d = -1.78), workload was lower (NASA-TLX 20.4 vs. 34.7; d = 0.90), and flow was higher (FSS 83.2 vs. 70.2; d = -1.23). Because the underlying problem, making a 3D selection through a 2D interface, is not specific to astronomy, we expect the pattern to carry over to point-cloud selection in other fields.
ER  -