Federico Galbiati

Before playing, gamers must wait for the game to load. While the effects of waiting on user quality of experience is well-studied for some domains, the effects of wait times on game players is not known, nor is the impact of computer system components, such as the processor or graphics card, on game loading times. We present results from a user study that evaluates the impact of game loading time on quality of experience using a custom tool that simulates game loading and collects player ratings. Analysis of the results shows game loading time has a pronounced effect on player quality of experience, but differs based on the individual game time and game load content. Results from our subsequent measurement experiment show the potential to reduce game load times through hardware upgrades – type of processor and graphics card have significant effects on game load times, but type of storage device less so.

To understand how well a proposed augmented reality (AR) solution works, existing papers often conducted tailored and isolated evaluations for specific AR tasks, e.g., depth or lighting estimation, and compared them to easy-to-setup baselines, either using datasets or resorting to time-consuming data capturing. Conceptually simple, it can be extremely difficult to evaluate an AR system fairly and in scale to understand its real-world performance. The difficulties arise for three key reasons: lack of control of the physical environment, the time-consuming data capturing, and the difficulties to reproduce baseline results. This paper presents our design of an AR experimentation platform, ExpAR, aiming to provide scalable and controllable AR experimentation. ExpAR is envisioned to operate as a standalone deployment or a federated platform; in the latter case, AR researchers can contribute physical resources, including scene setup and capturing devices, and allow others to time share these resources. Our design centers around the generic sensing-understanding-rendering pipeline and is driven by the evaluation limitations observed in recent AR systems papers. We demonstrate the feasibility of this vision with a preliminary prototype and our preliminary evaluations suggest the importance of further investigating different device capabilities to stream in 30 FPS.