The task of multiple object selection (MOS) in immersive virtual environments is important and still largely unexplored. The difficulty of efficient MOS increases with the number of objects to be selected. E.g. in small-scale MOS, only a few objects need to be simultaneously selected. This may be accomplished by serializing existing single-object selection techniques. In this paper, we explore various MOS tools for large-scale MOS. That is, when the number of objects to be selected are counted in hundreds, or even thousands. This makes serialization of single-object techniques prohibitively time consuming. Instead, we have implemented and tested two of the existing approaches to 3-D MOS, a brush and a lasso, as well as a new technique, a magic wand, which automatically selects objects based on local proximity to other objects. In a formal user evaluation, we have studied how the performance of the MOS tools are affected by the geometric configuration of the objects to be selected. Our studies demonstrate that the performance of MOS techniques is very significantly affected by the geometric scenario facing the user. Furthermore, we demonstrate that a good match between MOS tool shape and the geometric configuration is not always preferable, if the applied tool is complex to use.

No abstract

Tracking technologies are becoming an affordable commodity due to the wide use in mobile devices today. However, all tracking technologies available in commodity hardware is error prone due to problems such as drift, latency and jitter. The current understanding of human perception of static tracking errors is limited. This information about human perception might be useful in designing tracking systems for the display of AR and VR scenarios on commodity hardware. In this paper we present the findings of a study on the human perception of static orientation errors in a tracking system, using two different setups leveraging a handheld viewfinder: a classical augmented scenario and an indirect augmented one. By categorizing static orientation errors by scenario and local orientation axis, new insights into the users' ability to register orientational errors in the system are found. Our results show that users are much more aware of errors in classical AR scenarios in comparison to indirect AR scenarios. For both scenarios, the users registered roll orientation errors differently from both pitch and yaw orientation errors, and pitch and yaw perception is highly dependent on the scenario. However, the users performance ranking for orientational errors in AR scenarios was unexpected.

No abstract

No abstract