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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t-distributed Stochastic Neighbour Embedding (t-SNE) has become a standard for exploratory data analysis, as it is capable of revealing clusters even in complex data while requiring minimal user input. While its run-time complexity limited it to small datasets in the past, recent efforts improved upon the expensive similarity computations and the previously quadratic minimization. Nevertheless, t-SNE still has high runtime and memory costs when operating on millions of points. We present a novel method for executing the t-SNE minimization. While our method overall retains a linear runtime complexity, we obtain a significant performance increase in the most expensive part of the minimization. We achieve a significant improvement without a noticeable decrease in accuracy even when targeting a 3D embedding. Our method constructs a pair of spatial hierarchies over the embedding, which are simultaneously traversed to approximate many N-body interactions at once. We demonstrate an efficient GPGPU implementation and evaluate its performance against state-of-the-art methods on a variety of datasets.@en

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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Voxels are a popular choice to encode complex geometry. Their regularity makes updates easy and enables random retrieval of values. The main limitation lies in the poor scaling with respect to resolution. Sparse voxel DAGs (Directed Acyclic Graphs) overcome this hurdle and offer high-resolution representations for real-time rendering but only handle static data. We introduce a novel data structure to enable interactive modifications of such compressed voxel geometry without requiring de- and recompression. Besides binary data to encode geometry, it also supports compressed attributes (e.g., color). We illustrate the usefulness of our representation via an interactive large-scale voxel editor (supporting carving, filling, copying, and painting).

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Shadow volumes are a popular technique to compute pixel-accurate hard shadows in 3D scenes. Many variants exist that trade off accuracy and efficiency. In this work, we present an artifact-free, efficient, and easy-to-implement stencil shadow volume method. We compare our method to established stencil shadow volume techniques and show that it outperforms the alternatives.

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A camera's shutter controls the incoming light that is reaching the camera sensor. Different shutters lead to wildly different results, and are often used as a tool in movies for artistic purpose, e.g., they can indirectly control the effect of motion blur. However, a physical camera is limited to a single shutter setting at any given moment. ShutterApp enables users to define spatio-temporally-varying virtual shutters that go beyond the options available in real-world camera systems. A user provides a sparse set of annotations that define shutter functions at selected locations in key frames. From this input, our solution defines shutter functions for each pixel of the video sequence using a suitable interpolation technique, which are then employed to derive the output video. Our solution performs in real-time on commodity hardware. Hereby, users can explore different options interactively, leading to a new level of expressiveness without having to rely on specialized hardware or laborious editing.

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In this tutorial, we discuss voxel DAGs and multiresolution hierarchies, which are representations that can encode large volumes of data very efficiently. Despite a significant compression ration, an advantage of these structures is that their content can be efficiently accessed in real-time. This property enables various applications. We begin the tutorial by introducing the concepts of sparsity and of coherency in voxel structures, and explain how a directed acyclic graph (DAG) can be used to represent voxel geometry in a form that exploits both aspects, while remaining usable in its compressed from for e.g. ray casting. In this context, we also discuss extensions that cover the time domain or consider an advanced encoding strategies exploiting symmetries and entropy. We then move on to voxel attributes, such as colors, and explain how to integrate such information with the voxel DAGs. We will provide implementation details and present methods for efficiently constructing the DAGs and also cover how to efficiently access the data structures with e.g. GPU-based ray tracers. The course will be rounded of with a segment on applications. We highlight a few examples and show their results. Pre-computed shadows are a special application, which will be covered in detail. In this context, we also explain how some of previous ideas contribute to multi-resolution hierarchies, which gives an outlook on the potential generality of the presented solutions.@en