Optimal 3D selection technique assignment using real-time contextual analysis

Abstract

Selection in 3D virtual environments can vary wildly depending on the context of the selection. Various scene attributes such as object velocity and scene density will likely impact the user's ability to accurately select an object. While there are many existing 3D selection techniques that have been well studied, they all tend to be tailored to work best in a particular set of conditions, and may not perform well when these conditions are not met. As a result, designers must compromise by taking a holistic approach to choosing a primary technique; one which works well overall, but is possibly lacking in at least one scenario.We present a software framework that allows a flexible method of leveraging several selection techniques, each performing well under certain conditions. From these, the best one is utilized at any given moment to provide the user with an optimal selection experience across more scenarios and conditions. We performed a user study comparing our framework to two common 3D selection techniques, Bendcast and Expand. We evaluated the techniques across three levels of scene density and three levels of object velocity, collecting accuracy and timing data across a large sample of participants. From our results, we were able to conclude that our auto-selection technique approach is promising but there are several characteristics of the auto-selection process that can introduce drawbacks which need to be addressed and minimized.

Machine Learning Based Interaction Technique Selection for 3D User Interfaces

“…The technique is described in Figure 3(b). Our implementation is similar to the one described by Cashion et al [4], the Bendcast technique automatically bends to the closest target in order to help the user during the selection task. One difference in our implementation is that the ray bends only if the distance between the ray and the object is under a threshold.…”

Towards Naturally Grabbing and Moving Objects in VR

“…As indirect and direct interaction have their own benefits, techniques such as Bendcast and Expand that use both were proposed. Cashion et al [4] created an auto-select framework that contains techniques: Bendcast and Expand choose the most optimal technique in a given situation. The Bendcast technique selects the closest object to the cursor [4].…”

“…Cashion et al [4] created an auto-select framework that contains techniques: Bendcast and Expand choose the most optimal technique in a given situation. The Bendcast technique selects the closest object to the cursor [4]. Expand takes clones of all objects that are inside the cursor when the selection is triggered and place them into a virtual grid.…”

“…Expand takes clones of all objects that are inside the cursor when the selection is triggered and place them into a virtual grid. The virtual grid is spaced evenly to avoid object occlusion and collision, and is used for object selection [4,12], but the implementation uses a Play Station Move controller to control a cursor, instead of gestures. Of the three techniques, Bendcast performed the fastest, but had the highest error rate.…”

Poster: Dynamic adaptation of 3D selection techniques for suitability across diverse scenarios

“…In any case, it is important that the selection techniques utilized best match the expected environment and its conditions. With such importance placed on selection, it is no surprise that much work has been done to reduce selection time, reduce errors, and increase overall usability [6] [3]. Comprehensive research has been done on categorizing and grouping techniques by common features and characteristics [1].…”