AOGAN: A generative adversarial network for screen space ambient occlusion

Ambient occlusion (AO) is a widely-used real-time rendering technique which estimates light intensity on visible scene surfaces. Recently, a number of learning-based AO approaches have been proposed, which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and speed. However, most such methods have high error for complex scenes or tend to ignore details. We propose an end-to-end generative adversarial network for the production of realistic AO, and explore the importance of perceptual loss in the generative model to AO accuracy. An attention mechanism is also described to improve the accuracy of details, whose effectiveness is demonstrated on a wide variety of scenes.

Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering

Understanding and perceiving three-dimensional scientific visualizations, such as volume rendering, benefit from visual cues produced by the shading models. The conventional approaches are local shading models since they are computationally inexpensive and straightforward to implement. However, the local shading models do not always provide proper visual cues since non-local information is not sufficiently taken into account for the shading. Global illumination models achieve better visual cues, but they are often computationally expensive. It has been shown that alternative illumination models, such as ambient occlusion, multidirectional shading, and shadows, provide decent perceptual cues. Although these models improve upon local shading models, they still require expensive preprocessing, extra GPU memory, and a high computational cost, which cause a lack of interactivity during the transfer function manipulations and light position changes. In this paper, we proposed an approximate image-space multidirectional occlusion shading model for the volume rendering. Our model was computationally less expensive compared to the global illumination models and did not require preprocessing. Moreover, interactive transfer function manipulations and light position changes were achievable. Our model simulated a wide range of shading behaviors, such as ambient occlusion and soft and hard shadows, and can be effortlessly applied to existing rendering systems such as direct volume rendering. We showed that the suggested model enhanced the visual cues with modest computational costs.

Stochastic-Depth Ambient Occlusion

Ambient occlusion (AO) is a popular rendering technique that enhances depth perception and realism by darkening locations that are less exposed to ambient light (e.g., corners and creases). In real-time applications, screen-space variants, relying on the depth buffer, are used due to their high performance and good visual quality. However, these only take visible surfaces into account, resulting in inconsistencies, especially during motion. Stochastic-Depth Ambient Occlusion is a novel AO algorithm that accounts for occluded geometry by relying on a stochastic depth map, capturing multiple scene layers per pixel at random. Hereby, we efficiently gather missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while maintaining real-time performance. Our approach integrates well into existing rendering pipelines and improves the robustness of many different AO techniques, including multi-view solutions.

A Corner-Highlighting Method for Ambient Occlusion

Graphical user experiences are now ubiquitous features, and therefore widespread. Specifically, the computer graphics field and the game industry have been continually favoring the ambient occlusion post-processing method for its superb indirect light approximation and its effectiveness. Nonetheless of its canonical performance, its operation on non-occluded surfaces is often seen redundant and unfavorable. In this paper, we propose a new perspective to handle such issues by highlighting the corners where ambient occlusion is likely to occur. Potential illumination occlusions are highlighted by checking the corners of the surfaces in the screen-space. Our algorithm showed feasibility for renderers to avoid unwanted computations by achieving performance improvements of 15% to 28% acceleration, in comparison to the previous works.

Pyramid HBAO — a Scalable Horizon-based Ambient Occlusion Method

Screen-space Ambient Occlusion (SSAO) methods have become an integral part of the process of calculating global illumination effects in real-time applications. The use of ambient occlusion improves the perception of the geometry of the scene, and also makes a significant contribution to the realism of the rendered image. However, the problems of accuracy and efficiency of algorithms of calculating ambient occlusion remain relevant. Most of the existing methods have similar algorithmic complexity, what makes their use in real-time applications very limited. The performance issues of methods working in the screen space are particularly acute in the current growing spreadness of 4K (3840 x 2160 pixels) resolution of the rendered image. In this paper we provide our own algorithm Pyramid HBAO, which enhances the classic HBAO method by changing its calculation complexity for high resolution.