DrillSample

Abstract

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.