Screen-space ambient occlusion (SSAO) shows high efficiency and is widely used in real-time 3D applications. However, using SSAO algorithms in stereo rendering can lead to inconsistencies due to the differences in the screen-space information captured by the left and right eye. This will affect the perception of the scene and may be a source of viewer discomfort. In this paper, we show that the raw obscurance estimation part and subsequent filtering are both sources of inconsistencies. We developed a screen-space method involving both views in conjunction, leading to a stereo-aware raw obscurance estimation method and a stereo-aware bilateral filter. The results show that our method reduces stereo inconsistencies to a level comparable to geometry-based AO solutions, while maintaining the performance benefits of a screen-space approach.

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Eye-tracking with gaze estimation is a key element in many applications, ranging from foveated rendering and user interaction to behavioural analysis and usage metrics. For virtual reality, eye-tracking typically relies on near-eye cameras that are mounted in the VR headset. Such methods usually involve an initial calibration to create a mapping from eye features to a gaze position. However, the accuracy based on the initial calibration degrades when the position of the headset relative to the users’ head changes; this is especially noticeable when users readjust the headset for comfort or even completely remove it for a short while. We show that a correction of such shifts can be achieved via 2D drift vectors in eye space. Our method estimates these drifts by extracting salient cues from the shown virtual environment to determine potential gaze directions. Our solution can compensate for HMD shifts, even those arising from taking off the headset, which enables us to eliminate reinitialization steps.

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The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.@en