Locomotion in virtual environments is currently a difficult and unnatural task to perform. Normally, researchers tend to devise ground-floor-based metaphors, to constrain the degrees of freedom (DoFs) during motion. These restrictions enable interactions that accurately emulate human gait to provide high interaction fidelity. However, flying allows users to reach specific locations in a virtual scene more expeditiously. Our experience suggests that high-interaction fidelity techniques may also improve the flying experience, although it is not innate to humans, since it requires simultaneously controlling additional DoFs. We contribute with the Magic Carpet, an approach to flying that combines a floor-proxy with a full-body representation, to avoid imbalance and cybersickness issues. This design space allows to address direction indication and speed control as two separate phases of travel, thereby enabling techniques with higher interaction fidelity. To validate our design space, we developed two complementary studies, one for each of the travel phases. In this paper, we present the results of both studies within the Magic Carpet design space. To this end, we applied both objective and subjective measurements to determine the best set of techniques inside our design space. Our results show that this approach enables high-interaction fidelity techniques while improving user experience.

Three-dimensional (3D) imaging technologies have been increasingly explored in academia and the industrial sector, especially the ones yielding point clouds. However, obtaining these data can still be expensive and time-consuming, reducing the efficiency of procedures dependent on large datasets, such as the generation of data for machine learning training, forest canopy calculation, and subsea survey. A trending solution is developing simulators for imaging systems, performing the virtual scanning of the digital world, and generating synthetic point clouds from the targets. This work presents a guideline for the development of modular Light Detection and Ranging (LiDAR) system simulators based on parallel raycasting algorithms, with its sensor modeled by metrological parameters and error models. A procedure for calibrating the sensor is also presented, based on comparing with the measurements made by a commercial LiDAR sensor. The sensor simulator developed as a case study resulted in a robust generation of synthetic point clouds in different scenarios, enabling the creation of datasets for use in concept tests, combining real and virtual data, among other applications.