We present the design and construction of the Silo, a fully immersive stereoscopic cylindrical tiled-display visualization facility. Comprising 168 high-density LCD displays, the facility provides an ultra-high-resolution image of 619 million pixels, and close to 360 horizontal field-of-regards (FoR), aiming to maximize visual acuity and completely engage the human visual sensorium and its periphery. In this article, we outline the motivations, design principles, hardware selection and software systems, and interaction modalities used in constructing the Silo. To address missing visual information due to the absence of a ceiling and floor, we have designed a method that utilizes conformal mapping and optimal mass transport to reproject the entire 360 volumetric FoR of the virtual scene to the available display real estate. We showcase several applications demonstrating the utility of the Silo and report the findings of our user studies that highlight the effectiveness of the Silo layout compared to curved mono and flat powerwall display facilities. Our user evaluations and studies have shown that the Silo supports natural exploration and enhanced visualization due to its capability to render surround ultra-high-resolution stereoscopic views.

Understanding visual attention is key to designing efficient human-computer interaction, especially for virtual reality (VR) and augmented reality (AR) applications. However, the relationship between 3D spatial attributes of visual stimuli and visual attention is still underexplored. Thus, we design an experiment to collect a gaze dataset in VR, and use it to quantitatively model the probability of first attention between two stimuli. First, we construct the dataset by presenting subjects with a synthetic VR scene containing varying spatial configurations of two spheres. Second, we formulate their selective attention based on a probability model that takes as input two view-specific stimuli attributes: their eccentricities in the field of view and their sizes as visual angles. Third, we train two models using our gaze dataset to predict the probability distribution of a user's preferences of visual stimuli within the scene. We evaluate our method by comparing model performance across two challenging synthetic scenes in VR. Our application case study demonstrates that VR designers can utilize our models for attention prediction in two-foreground-object scenarios, which are common when designing 3D content for storytelling or scene guidance. We make the dataset and the source code to visualize it available alongside this work.