Open Access

Most Virtual Reality (VR) applications use rendering methods which implement local illumination models, simulating only direct interaction of light with 3D objects. They do not take into account the energy exchange between the objects themselves, making the resulting images look non-optimal. The main reason for this is the simulation of global illumination having a high computational complexity, decreasing the frame rate extremely. As a result this makes for example user interaction quite challenging. One way to decrease the time of image generation using rendering methods which implement global illumination models is to involve additional compute nodes in the process of image creation, distribute the rendering subtasks among these and then collate the results of the subtasks into a single image. Such a strategy is called distributed rendering. In this paper we introduce a software interface which gives a recommendation how the distributed rendering approach may be integrated into VR frameworks to achieve lower generation time of high quality, realistic images. The interface describes a client-server architecture which realizes the communication between visualization and compute nodes including data and rendering subtask distribution and may be used for the implementation of different load-balancing methods. We show an example of the implementation of the proposed interface in the context of realistic rendering of buildings for decisions on interior options.

We propose a high-performance GPU implementation of Ray Histogram Fusion (RHF), a denoising method for stochastic global illumination rendering. Based on the CPU implementation of the original algorithm, we present a naive GPU implementation and the necessary optimization steps. Eventually, we show that our optimizations increase the performance of RHF by two orders of magnitude when compared to the original CPU implementation and one order of magnitude compared to the naive GPU implementation. We show how the quality for identical rendering times relates to unfiltered path tracing and how much time is needed to achieve identical quality when compared to an unfiltered path traced result. Finally, we summarize our work and describe possible future applications and research based on this.

We present our work on phase space rendering. Every radiance sample in space has a location and a direction from which it is received. These degrees of freedom make up a phase space. The rendering problem of generating a discrete image from single radiance values is reduced to reconstruct a continuous radiance function from sparse samples in its phase space. The problem of reconstruction in a sparsely sampled space is solved by utilizing scattered data interpolation (SDI) methods. We provide numerical and visual evaluations of experiments with three SDI methods.

We present a real-time approximate simulation of some camera errors and the effects these errors have on some common computer vision algorithms for robots. The simulation uses a software framework for real-time post processing of image data. We analyse the performance of some basic algorithms for robotic vision when adding modifications to images due to camera errors. The result of each algorithm / error combination is presented. This simulation is useful to tune robotic algorithms to make them more robust to imperfections of real cameras.

Head-mounted displays with dense pixel arrays used for virtual reality applications require high frame rates and low latency rendering. This forms a challenging use case for any rendering approach. In addition to its ability of generating realistic images, ray tracing offers a number of distinct advantages, but has been held back mainly by its performance. In this paper, we present an approach that significantly improves image generation performance of ray tracing. This is done by combining foveated rendering based on eye tracking with reprojection rendering using previous frames in order to drastically reduce the number of new image samples per frame. To reproject samples a coarse geometry is reconstructed from a G-Buffer. Possible errors introduced by this reprojection as well as parts that are critical to the perception are scheduled for resampling. Additionally, a coarse color buffer is used to provide an initial image, refined smoothly by more samples were needed. Evaluations and user tests show that our method achieves real-time frame rates, while visual differences compared to fully rendered images are hardly perceivable. As a result, we can ray trace non-trivial static scenes for the Oculus DK2 HMD at 1182 × 1464 per eye within the the VSync limits without perceived visual differences.

Human beings spend much time under the influence of artificial lighting. Often, it is beneficial to adapt lighting to the task, as well as the user’s mental and physical constitution and well-being. This formulates new requirements for lighting - human-centric lighting - and drives a need for new light control methods in interior spaces. In this paper we present a holistic system that provides a novel approach to human-centric lighting by introducing simulation methods into interactive light control, to adapt the lighting based on the user's needs. We look at a simulation and evaluation platform that uses interactive stochastic spectral rendering methods to simulate light sources, allowing for their interactive adjustment and adaption.

The simulation of global illumination for Virtual Reality (VR) applications is a challenging process. The main reason for this is the high computational complexity of the Monte Carlo integration which makes sufficient frame rates hard to achieve. It is even more challenging to adopt this process for large, high-resolution displays (LHRD), because the resolution of an image to be generated becomes huge compared to a single display. One possibility to decrease the time of image rendering without worsening image quality is to involve additional computational nodes. The process of image creation has to be split into multiple rendering-tasks which may be computed independently. The resulting image data has to be conveyed to the display nodes where it is combined and passed to corresponding output devices. In this paper we introduce an extended version of our software interface which allows to integrate a flexible distributed rendering approach into VR frameworks, thus enabling high-quality realistic image generation on LHRDs. The interface describes a software architecture which realizes the communication between manager, computational and display nodes including rendering subtasks and data distribution and allows for the implementation of different load-balancing methods.

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

In recent years, a variety of methods have been introduced to exploit the decrease in visual acuity of peripheral vision, known as foveated rendering. As more and more computationally involved shading is requested and display resolutions increase, maintaining low latencies is challenging when rendering in a virtual reality context. Here, foveated rendering is a promising approach for reducing the number of shaded samples. However, besides the reduction of the visual acuity, the eye is an optical system, filtering radiance through lenses. The lenses create depth-of-field (DoF) effects when accommodated to objects at varying distances. The central idea of this article is to exploit these effects as a filtering method to conceal rendering artifacts. To showcase the potential of such filters, we present a foveated rendering system, tightly integrated with a gaze-contingent DoF filter. Besides presenting benchmarks of the DoF and rendering pipeline, we carried out a perceptual study, showing that rendering quality is rated almost on par with full rendering when using DoF in our foveated mode, while shaded samples are reduced by more than 69%.

Rendering techniques for design evaluation and review or for visualizing large volume data often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates. A view direction based rendering technique renders the users central field of view in high quality whereas the surrounding is rendered with a level of detail approach depending on the distance to the users central field of view thus giving the opportunity to increase rendering efficiency. We propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.

This work presents the analysis of data recorded by an eye tracking device in the course of evaluating a foveated rendering approach for head-mounted displays (HMDs). Foveated rendering methods adapt the image synthesis process to the user’s gaze and exploiting the human visual system’s limitations to increase rendering performance. Especially, foveated rendering has great potential when certain requirements have to be fulfilled, like low-latency rendering to cope with high display refresh rates. This is crucial for virtual reality (VR), as a high level of immersion, which can only be achieved with high rendering performance and also helps to reduce nausea, is an important factor in this field. We put things in context by first providing basic information about our rendering system, followed by a description of the user study and the collected data. This data stems from fixation tasks that subjects had to perform while being shown fly-through sequences of virtual scenes on an HMD. These fixation tasks consisted of a combination of various scenes and fixation modes. Besides static fixation targets, moving tar- gets on randomized paths as well as a free focus mode were tested. Using this data, we estimate the precision of the utilized eye tracker and analyze the participants’ accuracy in focusing the displayed fixation targets. Here, we also take a look at eccentricity-dependent quality ratings. Comparing this information with the users’ quality ratings given for the displayed sequences then reveals an interesting connection between fixation modes, fixation accuracy and quality ratings.