Increasing Selection Accuracy and Speed through Progressive Refinement

Although many selection techniques have been proposed and developed over the years, selection by pointing is perhaps the most popular approach for selection. In 3D interfaces, the laser-pointer metaphor is commonly used, since users only have to point to their target from a distance. However, the task of selecting objects that have a small visible area or that are in highly cluttered environments is hard when using pointing techniques. With both indirect and direct pointing techniques in 3D interfaces, smaller targets require higher levels of pointing precision from the user. In addition, issues such as target occlusion as well as hand and tracker jitter negatively affect user performance. Therefore, requiring the user to perform selection in a single precise step may result in users spending more time to select targets so that they can be more accurate (effect known as the speed-accuracy trade-off).

We describe an approach to address this issue, called Progressive Refinement. Instead of performing a single precise selection, users gradually reduce the set of selectable objects to reduce the required precision of the task. This approach, however, has an inherent trade-off when compared to immediate selection techniques. Progressive refinement requires a gradual process of selection, often using multiple steps, although each step can be fast, accurate, and nearly effortless. Immediate techniques, on the other hand, involve a single-step selection that requires effort and may be slower and more error-prone. Therefore, the goal of this work was to explore this trade-off. The research includes the design and evaluation of progressive refinement techniques for 3D interfaces, using both pointing- and gesture-based interfaces for single-object selection and volume selection.

Our technique designs and other existing selection techniques that can be classified as progressive refinement were used to create a design space. We designed eight progressive refinement techniques and compared them to the most commonly used techniques (for a baseline comparison) and to other state-of-the-art selection techniques in a total of four empirical studies. Based on the results of the studies, we developed a set of design guidelines that will help other researchers design and use progressive refinement techniques.