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. / Ph. D.

Distal pointing is the interaction style defined by directly pointing at targets from a distance. It follows a laser pointer metaphor and the position of the cursor is determined by the intersection of a vector extending the pointing device with the display surface. Distal pointing as a basic interaction style poses several challenges for the user, mainly because of the lack of precision humans have when using it. The focus of this thesis is to understand and improve distal pointing, making it a viable interaction metaphor to be used in a wide variety of applications. We achieve this by proposing and validating a predictive model of distal pointing that is inspired by Fitts' law, but which contains some unique features. The difficulty of a distal pointing task is best described by the angular size of the target and the angular distance that the cursor needs to go across to reach the target from the input device perspective. The practical impact of this is that the user's relative position to the target should be taken into account. Based on the model we derived, we proposed a set of design guidelines for high-precision distal pointing techniques. The main guideline from the model is that increasing the target size is much more important than reducing the distance to the target. In order to improve distal pointing, we followed the model guidelines and designed interaction techniques that aim at improving the precision of distal pointing tasks. Absolute and Relative Mapping (ARM) distal pointing increases precision by offering the user a toggle which changes the control/display (CD) ratio such that a large movement of the input device is mapped to a small movement of the cursor. Dynamic Control Display Ratio (DyCoDiR) automatically increases distal pointing precision, as the user needs it. DyCoDiR takes into account the user distance to the interaction area and the speed at which the user moves the input device to dynamically calculate an increased CD ratio, making the action more precise the steadier the user tries to be. We performed an evaluation of ARM and DyCoDiR comparing them to basic distal pointing in a realistic context. In this experiment, we also provided variations of the techniques which increased the visual perception of targets through zooming in the area around the cursor when precision was needed. Results from the study show that ARM and DyCoDiR are significantly faster and more accurate than basic distal pointing with tasks that require very high precision. We analyzed user navigation strategies and found that the high precision techniques afford users to remain stationary while performing interactions. However, we also found that individual differences have a strong impact on the decision to walk or not, and that, sometimes, is more important than the technique affordance. We provided a validation for the distal pointing model through the analysis of expected difficulty of distal pointing tasks in light of each technique tested. We propose selection by progressive refinement, a new design concept for distal pointing selection techniques, whose goal is to offer the ability to achieve near perfect accuracy in selection at very cluttered environments. The idea of selection by progressive refinement is to gradually eliminate possible targets from the set of selectable objects until only one object is available for selection. We implemented SQUAD, a selection by progressive refinement distal pointing technique, and performed a controlled experiment comparing it to basic distal pointing. We found that there is a clear tradeoff between immediate selections that require high precision and selections by progressive refinement which always require low precision. We validated the model by fitting the distal pointing data and proposed a new model, which has a linear growth in time, for SQUAD selection. / Ph. D.