Starfish: a selection technique for dense virtual environments
Proceedings of the 18th ACM symposium on Virtual reality software and technology
DrillSample: precise selection in dense handheld augmented reality environments
Proceedings of the Virtual Reality International Conference: Laval Virtual
Design and evaluation of 3D selection techniques based on progressive refinement
International Journal of Human-Computer Studies
Hi-index | 0.00 |
3D object selection is more demanding when, 1) objects densly surround the target object, 2) the target object is significantly occluded, and 3) when the target object is dynamically changing location. Most 3D selection techniques and guidelines were developed and tested on static or mostly sparse environments. In contrast, games tend to incorporate densly packed and dynamic objects as part of their typical interaction. With the increasing popularity of 3D selection in games using hand gestures or motion controllers, our current understanding of 3D selection needs revision. We present a study that compared four different selection techniques under five different scenarios based on varying object density and motion dynamics. We utilized two existing techniques, Raycasting and SQUAD, and developed two variations of them, Zoom and Expand, using iterative design. Our results indicate that while Raycasting and SQUAD both have weaknesses in terms of speed and accuracy in dense and dynamic environments, by making small modifications to them (i.e., flavoring), we can achieve significant performance increases.