The go-go interaction technique: non-linear mapping for direct manipulation in VR
Proceedings of the 9th annual ACM symposium on User interface software and technology
Aperture based selection for immersive virtual environments
Proceedings of the 9th annual ACM symposium on User interface software and technology
Proceedings of the 1997 symposium on Interactive 3D graphics
Image plane interaction techniques in 3D immersive environments
Proceedings of the 1997 symposium on Interactive 3D graphics
3D User Interfaces: Theory and Practice
3D User Interfaces: Theory and Practice
Precise selection techniques for multi-touch screens
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Freeze-Set-Go interaction method for handheld mobile augmented reality environments
Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology
Multimodal interaction concepts for mobile augmented reality applications
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Rapid and accurate 3D selection by progressive refinement
3DUI '11 Proceedings of the 2011 IEEE Symposium on 3D User Interfaces
Dense and Dynamic 3D Selection for Game-Based Virtual Environments
IEEE Transactions on Visualization and Computer Graphics
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One of the primary tasks in a dense mobile augmented reality (AR) environment is to ensure precise selection of an object, even if it is occluded or highly similar to surrounding virtual scene objects. Existing interaction techniques for mobile AR usually use the multi-touch capabilities of the device for object selection. However, single touch input is imprecise, but existing two handed selection techniques to increase selection accuracy do not apply for one-handed handheld AR environments. To address the requirements of accurate selection in a one-handed dense handheld AR environment, we present the novel selection technique DrillSample. It requires only single touch input for selection and preserves the full original spatial context of the selected objects. This allows disambiguating and selection of strongly occluded objects or of objects with high similarity in visual appearance. In a comprehensive user study, we compare two existing selection techniques with DrillSample to explore performance, usability and accuracy. The results of the study indicate that DrillSampe achieves significant performance increases in terms of speed and accuracy. Since existing selection techniques are designed for virtual environments (VEs), we furthermore provide a first approach towards a foundation for exploring 3D selection techniques in dense handheld AR.