Rendering high-resolution photorealistic images in real time is challenging for video games and emerging virtual reality headsets. Thus, fovea sampled image reconstruction and super-resolution technologies become more and more crucial. However, most current methods process foveated reconstruction and super-resolution separately, which is slow. To address this issue, we propose a novel multi-scale spatiotemporal kernel prediction network for real-time foveated rendering that can perform sparse peripheral region reconstruction and supersampling simultaneously, resulting in a substantial reduction in rendering computation without visually noticeable quality degradation. Thanks to the multiscale kernel prediction architecture, different levels of details can be effectively preserved. Furthermore, we introduce an effective motion vector mask to explicitly identify occluded regions, which can help to use historical information more effectively. Our network runs in real time and achieves superior image quality and better inter-frame stability than existing methods.

Soft shadows play a crucial role in enhancing visual realism in real-time rendering. Although traditional shadow mapping techniques offer high efficiency, they often suffer from artifacts and limited quality. In contrast, ray tracing can produce high-fidelity soft shadows but incurs substantial computational cost. In this paper, we propose a general-purpose, real-time soft shadow generation method based on neural networks. To encode shadow geometry, we employ the hard shadows via shadow mapping as input to our network, which effectively captures the spatial layout of shadow positions and contours. A lightweight U-Net architecture then refines this input to synthesize high-quality soft shadows in real time. The generated shadows closely approximate ray-traced references in visual fidelity. Compared to existing learning-based methods, our approach produces higher-quality soft shadows and offers improved generalization across diverse scenes. Furthermore, it requires no scene-specific precomputation, making it directly applicable to practical real-time rendering scenarios.

With the proliferation of mobile hardware-accelerated ray tracing, visual quality at low sampling rates (1spp) significantly deteriorates due to high-frequency noise and temporal artifacts introduced by Monte Carlo path tracing. Traditional spatiotemporal denoising methods, such as Spatiotemporal Variance-Guided Filtering (SVGF), effectively suppress noise by fusing multi-frame information and using geometry buffer (G-buffer) guided filters. However, their reliance on per-frame variance computation and global filtering imposes prohibitive overhead for mobile devices. This paper proposes an edge-aware, data-driven real-time denoising architecture within the SVGF framework, tailored explicitly for mobile computational constraints. Our method introduces two key innovations that eliminate variance estimation overhead: (1) an adaptive filtering kernel sizing mechanism, which dynamically adjusts filtering scope based on local complexity analysis of the G-buffer; and (2) a data-driven weight table construction strategy, converting traditional computational processes into efficient real-time lookup operations. These innovations significantly enhance processing efficiency while preserving edge accuracy. Experimental results on the Qualcomm Snapdragon 768G platform demonstrate that our method achieves 55 FPS with 1spp input. This frame rate is 67.42% higher than mobile-optimized SVGF, provides better visual quality , and reduces power consumption by 16.80% . Our solution offers a practical and efficient denoising framework suitable for real-time ray tracing in mobile gaming and AR/VR applications.

With technological advancements, virtual reality (VR), once limited to high-end professional applications, is rapidly expanding into entertainment and broader consumer domains. However, the inherent contradiction between mobile hardware computing power and the demand for high-resolution, high-refresh-rate rendering has intensified, leading to critical bottlenecks, including frame latency and power overload, which constrain large-scale applications of VR systems. This study systematically analyzes four key technologies for efficient VR rendering: (1) foveated rendering, which dynamically reduces rendering precision in peripheral regions based on the physiological characteristics of the human visual system (HVS), thereby significantly decreasing graphics computation load; (2) stereo rendering, optimized through consistent stereo rendering acceleration algorithms; (3) cloud rendering, utilizing object-based decomposition and illumination-based decomposition for distributed resource scheduling; and (4) low-power rendering, integrating parameter-optimized rendering, super-resolution technology, and frame-generation technology to enhance mobile energy efficiency. Through a systematic review of the core principles and optimization approaches of these technologies, this study establishes research benchmarks for developing efficient VR systems that achieve high fidelity and low latency while providing further theoretical support for the engineering implementation and industrial advancement of VR rendering technologies.

Abstract Screen Space Reflection (SSR) can reliably achieve highly efficient reflective effects, significantly enhancing users' sense of realism in real‐time applications. However, when directly applied to stereo rendering, popular SSR algorithms lead to inconsistencies due to the differing information between the left and right eyes. This inconsistency, invisible to human vision, results in visual discomfort. This paper analyzes and demonstrates how screen‐space geometries, fade boundaries, and reflection samples introduce inconsistent cues. Considering the complementary nature of screen information, we introduce a stereo‐aware SSR method to alleviate visual discomfort caused by screen space disparities. By contrasting our stereo‐aware SSR with conventional SSR and ray‐traced results, we showcase the effectiveness of our approach in mitigating the inconsistencies stemming from screen space differences while introducing affordable performance overhead for real‐time rendering.

Various temporal denoising methods have been proposed to clean up the noise for real-time ray tracing (RTRT). These methods rely on the temporal correspondences of pixels between the current and previous frames, i.e. per-pixel screen-space motion vectors. However, the state-of-the-art temporal reuse methods with traditional motion vectors cause artifacts in motion occlusions. We accordingly propose a novel neural temporal denoising method for indirect illumination of Monte Carlo (MC) ray tracing at 1 sample per pixel. Based on end-to-end multi-scale kernel-based reconstruction, we apply temporally reliable dual motion vectors to facilitate better reconstruction of the occlusions, and also introduce additional motion occlusion loss to reduce ghosting artifacts. Experiments show that our method significantly reduces the over-blurring and ghosting artifacts while generating high-quality images at real-time rates.

Abstract Normal map‐based microstructure rendering can generate both glint and scratch appearance accurately. However, the extra high‐resolution normal map that defines every microfacet normal may incur high storage and computation costs. We present an example‐based real‐time rendering method for arbitrary microstructure materials, which significantly reduces the required storage space. Our method takes a small‐size normal map sample as input. We implicitly synthesize a high‐resolution normal map from the normal map sample and construct MIP‐mapped 4D position‐normal Gaussian lobes. Based on the above MIP‐mapped 4D lobes and a LUT (lookup table) data structure for the synthesized high‐resolution normal map, an efficient Gaussian query method is presented to evaluate ‐NDFs (position‐normal distribution functions) for shading. We can render complex scenes with glint and scratch surfaces in real time ( 30 fps) with a full high‐definition resolution, and the space required for each microstructure material is decreased to 30 MB.

The interaction between light and materials is key to physically-based realistic rendering. However, it is also complex to analyze, especially when the materials contain a large number of details and thus exhibit “glinty” visual effects. Recent methods of producing glinty appearance are expected to be important in next-generation computer graphics. We provide here a comprehensive survey on recent glinty appearance rendering. We start with a definition of glinty appearance based on microfacet theory, and then summarize research works in terms of representation and practical rendering. We have implemented typical methods using our unified platform and compare them in terms of visual effects, rendering speed, and memory consumption. Finally, we briefly discuss limitations and future research directions. We hope our analysis, implementations, and comparisons will provide insight for readers hoping to choose suitable methods for applications, or carry out research.

Fur appearance rendering is crucial for the realism of computer generated imagery, but is also a challenge in computer graphics for many years. Much effort has been made to accurately simulate the multiple-scattered light transport among fur fibers, but the computation cost is still very high, since the number of fur fibers is usually extremely large. In this paper, we aim at reducing the number of fur fibers while preserving realistic fur appearance. We present an aggregated fur appearance model, using one thick cylinder to accurately describe the aggregated optical behavior of a bunch of fur fibers, including the multiple scattering of light among them. Then, to acquire the parameters of our aggregated model, we use a lightweight neural network to map individual fur fiber's optical properties to those in our aggregated model. Finally, we come up with a practical heuristic that guides the simplification process of fur dynamically at different bounces of the light, leading to a practical level-of-detail rendering scheme. Our method achieves nearly the same results as the ground truth, but performs 3.8×-13.5× faster.

Abstract Gradient‐domain rendering can highly improve the convergence of light transport simulation using the smoothness in image space. These methods generate image gradients and solve an image reconstruction problem with rendered image and the gradient images. Recently, a previous work proposed a gradient‐domain volumetric photon density estimation for homogeneous participating media. However, the image reconstruction relies on traditional L1 reconstruction, which leads to obvious artifacts when only a few rendering passes are performed. Deep learning based reconstruction methods have been exploited for surface rendering, but they are not suitable for volume density estimation. In this paper, we propose an unsupervised neural network for image reconstruction of gradient‐domain volumetric photon density estimation, more specifically for volumetric photon mapping, using a variant of GradNet with an encoded shift connection and a separated auxiliary feature branch, which includes volume based auxiliary features such as transmittance and photon density. Our network smooths the images on global scale and preserves the high frequency details on a small scale. We demonstrate that our network produces a higher quality result, compared to previous work. Although we only considered volumetric photon mapping, it's straightforward to extend our method for other forms, like beam radiance estimation.

Abstract Microfacet theory is commonly used to build reflectance models for surfaces. While traditional microfacet‐based models assume that the distribution of a surface's microstructure is continuous, recent studies indicate that some surfaces with tiny, discrete and stochastic facets exhibit glittering visual effects, while some surfaces with structured features exhibit anisotropic specular reflection. Accordingly, this paper proposes an efficient and stationary method of surface material modeling to process both glittery and non‐glittery surfaces in a consistent way. Our method comprises two steps: in the preprocessing step, we take a fixed‐size sample normal map as input, then organize 4D microfacet trees in position and normal space for arbitrary‐sized surfaces; we also cluster microfacets into 4D K‐lobes via the adaptive k‐means method. In the rendering step, moreover, surface normals can be efficiently evaluated using pre‐clustered microfacets. Our method is able to efficiently render any structured, discrete and continuous micro‐surfaces using a precisely reconstructed surface NDF. Our method is both faster and uses less memory compared to the state‐of‐the‐art glittery surface modeling works.

Background Due to the restriction of display mode, in most of the virtual reality systems with multiple people in the same physical space , the program renders the scene based on the position and perspective of the one user, so that other users just see the same scene, resulting in vision disorder. Methods To improve experience of multi-user co-located collaboration, in this study, we propose a fire drill system supporting co-located collaboration, in which three co-located users can collaborate to complete the virtual firefighting mission. Firstly, with multi-view stereoscopic projective display technology and ultra wideband (UWB) technology, co-located users can roam independently and watch virtual scenes through the correct perspective view based on their own position by wearing dedicated shutter glasses, thus carrying out different virtual tasks, which improves the flexibility of co-located collaboration. Secondly, we design simulated firefighting water-gun using the micro-electromechanical system sensor, through which users can interact with virtual environment, and thus provide a better interactive experience. Finally, we develop a workbench including a holographic display module and multi-touch operation module for virtual scene assembly and virtual environment control. Results The controller can use the workbench to adjust the virtual layout in real time, and control the virtual task process to increase the flexibility and playability of system. Conclusions Our work can be employed in a wide range of related virtual reality applications .

Progressive photon mapping [Hachisuka et al. 2008] (PPM) obtains increasingly accurate results with progressive visualization, but it is problematic when results are obtained through thousands of iterations. An uniform photon distribution is critical for the accurate result. In this work, we use the sample elimination [Yuksel 2015] (SE) in PPM to achieve optimal results and accelerate the iterations. Sample elimination can produce sample sets with more pronounced blue noise characteristics. We apply the feature of this elimination method to the progressive iterations.

Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding rendering. It is an active research field that has been developed over the past two decades. The deficiency of precise details and efficient rendering are still the main challenges of photon mapping. This report reviews recent work and classifies it into a set of categories including radiance estimation, photon relaxation, photon tracing, progressive photon mapping, and parallel methods. The goals of our report are giving readers an overall introduction to photon mapping and motivating further research to address the limitations of existing methods.

The 3D GIS for high-speed rail's construction schedule needs to visualize the model data such as bridges, piers, roads, tunnels and so on, besides the texture data for example stone texture, metal texture, green belt texture and so forth. The high-speed rail GIS cannot render enormous 3D models in real time. 3D models are loaded slowly and rendered nonfluency. The paper focuses on showing 3D models based on LOD (level of detail), loading models incrementally, sharing the pier models and textures to reduce memory footprint. Because of the winding railway line, bullet train jumps to move ahead and deviates from its normal 3D rail path. We propose a method of how to update the bullet train's position and direction. The experiment results show that the3D models are rendered in real time and the train runs smoothly and steadily.

Point based rendering can simulate global illumination phenomenon fast and is widely used in movie production. This paper proposes a faster point based global illumination algorithm compared with the standard one. During our algorithm, the shading points generated by ray tracing are clustered in to groups. When doing point based rendering, the indirect illumination of each group's center point is computed instead of each shading point. We use an error strategy to make the indirect illumination compute adaptively. This method is several times faster than the standard point based global illumination method, while preserves high rendering quality. What's more, we implement our algorithm on GPU. In order to improve parallelism, we design a GPU-based cluster method and then implement the point based rendering process on GPU. The speed is times faster compared with the CPU one.

A semantic Web based model for product cooperative design is proposed by enabling engineering knowledge to be published, discovered, reused, and integrated. Three features of the model are described in detail. The first is the tools for Web design, which enable a design process to be published as a service and then to be reused as a design resource in other design processes; the second is the ontology of design resources, which helps the implementation of intelligent retrieval by semantic inference; the third is design workflows, which enable designers to construct complex design with the existing simple design instances.

This paper mainly discusses how to design a virtual museum system used by many users at the same time, and how to manage the scene data. To a 3D indoor scene, the empty scene (only the building) without other objects such as collections can be created by sweeping a 2D polygon along an axis which is perpendicular to the horizontal plane. We can create the scene polygon's Voronoi diagram, which subdivides the polygon into some cells called Voronoi regions by the proximity characteristic. An edge of the polygon corresponding to a wall of the building has its own Voronoi region where all the points are nearest to the edge than others. Hence, every user can select one Voronoi region to lay out collections such as cultural relics on the exhibition platform and every collection is saved by the Voronoi region in which it is laid out. Based on the scene polygon's Voronoi diagram, the system can also support multi-user cooperative interaction in the created scene very well. The work also can be used to create virtual art gallery, product exhibition hall and so on.

This paper describes a system framework of 3D virtual museum supporting multi-user cooperative interaction. To a 3D virtual museum, the building (empty scene) can be projected to a 2D polygon. Hence, we can manage the data of collections and users based on the scene polygon's Voronoi diagram. We can get the Voronoi diagram by subdividing the polygon into some cells using the proximity characteristic. We also establish some communication rules between users. In this article, how to compute visibility and detect collisions based on polygon's Voronoi diagram is discussed and this method is aimed to improve the reality and coherence of cooperative interaction in virtual museum.