LOD construction is typically implemented as a preprocessing step that requires users to wait before they are able to view the results in real time. We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still being loaded from disk. LOD construction and rendering are both implemented in CUDA and share the GPU's processing power, but each incremental update is lightweight enough to leave enough time to maintain real-time frame rates. Our approach is able to stream points from an SSD and update the octree on the GPU at rates of up to 580 million points per second (~9.3GB/s) on an RTX 4090 and a PCIe 5.0 SSD. Depending on the data set, our approach spends an average of about 1 to 2 ms to incrementally insert 1 million points into the octree, allowing us to insert several million points per frame into the LOD structure and render the intermediate results within the same frame.

The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably produces noise in rendered images for a practical number of samples per pixel. The problem of denoising these images has been widely studied, with most recent methods relying on data-driven, pretrained neural networks. In contrast, in this paper we propose a statistical approach to the denoising problem, treating each pixel as a random variable and reasoning about its distribution. Considering a pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based on online estimators—to decide which of the nearby pixels to exclude from the denoising filter. We show that for symmetric pixel weights and normally distributed samples, the classical Welch t-test is optimal in terms of mean squared error. We then show how to extend this result to handle non-normal distributions, using more recent confidence-interval formulations in combination with the Box-Cox transformation. Our results show that our statistical denoising approach matches the performance of state-of-the-art neural image denoising without having to resort to any computation-intensive pretraining. Furthermore, our approach easily generalizes to other quantities besides pixel intensity, which we demonstrate by showing additional applications to Russian roulette path termination and multiple importance sampling.

We introduce the Stability-Incorporated Neighborhood Graph (SING), a novel density-aware structure designed to capture the intrinsic geometric properties of a point set. We improve upon the spheres-of-influence graph by incorporating additional features to offer more flexibility and control in encoding proximity information and capturing local density variations. Through persistence analysis on our proximity graph, we propose a new clustering technique and explore additional variants incorporating extra features for the proximity criterion. Alongside the detailed analysis and comparison to evaluate its performance on various datasets, our experiments demonstrate that the proposed method can effectively extract meaningful clusters from diverse datasets with variations in density and correlation. Our application scenarios underscore the advantages of the proposed graph over classical neighborhood graphs, particularly in terms of parameter tuning.

For over two decades, the OpenGL API provided users with the means for implementing versatile, feature-rich, and portable real-time graphics applications. Consequently, it has been widely adopted by practitioners and educators alike and is deeply ingrained in many curricula that teach real-time graphics for higher education. Over the years, the architecture of graphics processing units (GPUs) incrementally diverged from OpenGL's conceptual design. The more recently introduced Vulkan API provides a more modern, fine-grained approach for interfacing with the GPU, which allows a high level of controllability and, thereby, deep insights into the inner workings of modern GPUs. This property makes the Vulkan API especially well suitable for teaching graphics programming in university education, where fundamental knowledge shall be conveyed. Hence, it stands to reason that educators who have their students' best interests at heart should provide them with corresponding lecture material. However, Vulkan is notoriously verbose and rather challenging for first-time users, thus transitioning to this new API bears a considerable risk of failing to achieve expected teaching goals. In this paper, we document our experiences after teaching Vulkan in both introductory and advanced graphics courses side-by-side with conventional OpenGL. A collection of surveys enables us to draw conclusions about perceived workload, difficulty, and students' acceptance of either approach. In doing so, we identify suitable conditions and recommendations for teaching Vulkan to both undergraduate and graduate students.

Architectural design and urban planning are complex design tasks. Predicting the thermal impact of design choices at interactive rates enhances the ability of designers to improve energy efficiency and avoid problematic heat islands while maintaining design quality. We show how to use and adapt methods from computer graphics to efficiently simulate heat transfer via thermal radiation, thereby improving user guidance in the early design phase of large-scale construction projects and helping to increase energy efficiency and outdoor comfort. Our method combines a hardware-accelerated photon tracing approach with a carefully selected finite element discretization, inspired by precomputed radiance transfer. This combination allows us to precompute a radiative transport operator, which we then use to rapidly solve either steady-state or transient heat transport throughout the entire scene. Our formulation integrates time-dependent solar irradiation data without requiring changes in the transport operator, allowing us to quickly analyze many different scenarios such as common weather patterns, monthly or yearly averages, or transient simulations spanning multiple days or weeks. We show how our approach can be used for interactive design workflows such as city planning via fast feedback in the early design phase.

With the recent advances in real-time rendering that were achieved by embracing software rasterization, the interest in alternative solutions for other fixed-function pipeline stages rises. In this paper, we revisit a recently presented software approach for cached tessellation, which compactly encodes and stores triangles in GPU memory. While the proposed technique is both efficient and versatile, we show that the original encoding is suboptimal and provide an alternative scheme that acts as a drop-in replacement. As shown in our evaluation, the proposed modifications can yield performance gains of 40% and more.

Determining connectivity between points and reconstructing their shape boundaries are long‐standing problems in computer graphics. One possible approach to solve these problems is to use a proximity graph. We propose a new proximity graph computed by intersecting the to‐date rarely used proximity‐based graph called spheres‐of‐influence graph (SIG) with the Delaunay triangulation (DT). We prove that the resulting graph, which we name SIGDT, contains the piece‐wise linear reconstruction for a set of unstructured points in the plane for a sampling condition superseding current bounds and capturing well practical point sets' properties. As an application, we apply a dual of boundary adjustment steps from the C onnect 2D algorithm to remove the redundant edges. We show that the resulting algorithm SIG‐C onnect 2D yields the best reconstruction accuracy compared to state‐of‐the‐art algorithms from a recent comprehensive benchmark, and the method offers the potential for further improvements, e.g., for surface reconstruction.

In most naturally occurring superconductors, electrons with opposite spins form Cooper pairs. This includes both conventional s-wave superconductors such as aluminium, as well as high-transition-temperature, d-wave superconductors. Materials with intrinsic p-wave superconductivity, hosting Cooper pairs made of equal-spin electrons, have not been conclusively identified, nor synthesized, despite promising progress1–3. Instead, engineered platforms where s-wave superconductors are brought into contact with magnetic materials have shown convincing signatures of equal-spin pairing4–6. Here we directly measure equal-spin pairing between spin-polarized quantum dots. This pairing is proximity-induced from an s-wave superconductor into a semiconducting nanowire with strong spin–orbit interaction. We demonstrate such pairing by showing that breaking a Cooper pair can result in two electrons with equal spin polarization. Our results demonstrate controllable detection of singlet and triplet pairing between the quantum dots. Achieving such triplet pairing in a sequence of quantum dots will be required for realizing an artificial Kitaev chain7–9. Controllable detection of singlet and triplet Cooper pair splitting via crossed Andreev reflection is demonstrated in spin-polarized quantum dots on a superconducting nanowire platform with strong spin–orbit coupling.

Large-scale unstructured point cloud scenes can be quickly visualized without prior reconstruction by utilizing levels-of-detail structures to load an appropriate subset from out-of-core storage for rendering the current view. However, as soon as we need structures within the point cloud, e.g., for interactions between objects, the construction of state-of-the-art data structures requires O(NlogN) time for N points, which is not feasible in real time for millions of points that are possibly updated in each frame. Therefore, we propose to use a surface representation structure which trades off the (here negligible) disadvantage of single-frame use for both output-dominated and near-linear construction time in practice, exploiting the inherent 2D property of sampled surfaces in 3D. This structure tightly encompasses the assumed surface of unstructured points in a set of bounding depth intervals for each cell of a discrete 2D grid. The sorted depth samples in the structure permit fast surface queries, and on top of that an occlusion graph for the scene comes almost for free. This graph enables novel real-time user operations such as revealing partially occluded objects, or scrolling through layers of occluding objects, e.g., walls in a building. As an example application we showcase a 3D scene exploration framework that enables fast, more sophisticated interactions with point clouds rendered in real time.

We present temporal-scope grammars for automatic composition of polyphonic music. In the context of this work, polyphony can refer to any arrangement of musical entities (notes, chords, measures, etc.) that is not purely sequential in the time dimension. Given that the natural output of a grammar is a sequence, the generation of sequential structures, such as melodies, harmonic progressions, and rhythmic patterns, follows intuitively. By contrast, we associate each musical entity with an independent temporal scope, allowing the representation of arbitrary note arrangements on every level of the grammar. With overlapping entities we can model chords, drum patterns, and parallel voices -- polyphony on small and large scales. We further propose the propagation of sub-grammar results through the derivation tree for synchronizing independently generated voices. For example, we can synchronize the notes of a melody and bass line by reading from a shared harmonic progression.

: Contemporary applications such as those within Augmented or Virtual Reality (AR/VR) pose challenges for software architectures supporting them, which have to adhere to stringent latency, data transmission, and performance requirements. This manifests in processing 3D models, whose 3D contents are increasingly generated procedurally rather than explicitly, resulting in computational workloads (i.e., perceived as Procedural Geometry Workloads) with particular characteristics and resource requirements. Traditionally, executing such workloads takes place in resource-rich environments such as the cloud. However, the massive amount of data transfer, heterogeneous devices, and networks involved affect latency, which in turn causes low-quality visualization in user-facing applications (e.g., AR/VR). To overcome such challenges, processing elements available close to end users can be leveraged to generate 3D models instead, and as such the edge emerges as a central architectural entity. This paper describes such procedural geometry workloads, their particular characteristics, and challenges to execute them on heterogeneous devices. Furthermore, we propose an architecture capable of provisioning procedural geometry workloads in edge scenarios.

Procedural generation has become a key component in satisfying a growing demand for ever-larger, highly detailed geometry in realistic, open-world games and simulations. In this paper, we present our work towards a new level-of-detail mechanism for procedural geometry shape grammars. Our approach automatically identifies and adds suitable surrogate rules to a shape grammar’s derivation tree. Opportunities for surrogates are detected in a dedicated pre-processing stage. Where suitable, textured impostors are then used for rendering based on the current viewpoint at runtime. Our proposed methods generate simplified geometry with superior visual quality to the state-of-the-art and roughly the same rendering performance.

We propose two novel methods to improve the efficiency and quality of real-time rendering applications: Texel differential-based content-adaptive shading (TDCAS) and spatio-temporally filtered adaptive shading (STeFAS). Utilizing Variable Rate Shading (VRS)—a hardware feature introduced with NVIDIA’s Turing micro-architecture—and properties derived during rendering or Temporal Anti-Aliasing (TAA), our techniques adapt the resolution to improve the performance and quality of real-time applications. VRS enables different shading resolution for different regions of the screen during a single render pass. In contrast to other techniques, TDCAS and STeFAS have very little overhead for computing the shading rate. STeFAS enables up to 4x higher rendering resolutions for similar frame rates, or a performance increase of 4 × at the same resolution.

Light-field cameras are becoming more popular in the consumer market. Their data redundancy allows, in theory, to accurately refocus images after acquisition and to predict the depth of each point visible from the camera. Combined, these two features allow for the generation of full-focus images, which is impossible in traditional cameras. Multiple methods for depth prediction from light fields (or stereo) have been proposed over the years. A large subset of these methods relies on cost-volume estimates - 3D objects where each layer represents a heuristic of whether each point in the image is at a certain distance from the camera. Generally, this volume is used to regress a depth map, which is then refined for better results. In this paper, we argue that refining the cost volumes is superior to refining the depth maps in order to further increase the accuracy of depth predictions. We propose a set of cost-volume refinement algorithms and show their effectiveness.

In this paper, we propose a new approach for the comparison and analysis of Monte Carlo (MC) rendering algorithms. It is based on a novel similarity measure called render score (RS) that is speci cally designed for MC rendering, statistically motivated, and incorporates bias and variance. Additionally, we propose a comparison scheme that alleviates the need for practically converged reference images (RIs). Our approach can be used to compare and analyze di erent rendering methods by revealing detailed (per-pixel) di erences and subsequently potential conceptual or implementation-related issues, thereby o ering a more informative and meaningful alternative to commonly used metrics.

Physically based rendering is a discipline in computer graphics which aims at reproducing certain light and material appearances that occur in the real world. Complex scenes can be difficult to compute for rendering algorithms. This paper introduces a new comprehensive test database of scenes that treat different light setups in conjunction with diverse materials and discusses its design principles. A lot of research is focused on the development of new algorithms that can deal with difficult light conditions and materials efficiently. This database delivers a comprehensive foundation for evaluating existing and newly developed rendering techniques. A final evaluation compares different results of different rendering algorithms for all scenes.

The perception of light is inherently different inside a virtual reality (VR) or augmented reality (AR) simulation when compared to the real world. Conventional head-worn displays (HWDs) are not able to display the same high dynamic range of brightness and color as the human eye can perceive in the real world. To mimic the perception of real-world scenes in virtual scenes, it is crucial to reproduce the effects of incident light on the human visual system. In order to advance virtual simulations towards perceptual realism, we present an eye-tracked VR/AR simulation comprising effects for gaze-dependent temporal eye adaption, perceptual glare, visual acuity reduction, and scotopic color vision. Our simulation is based on medical expert knowledge and medical studies of the healthy human eye. We conducted the first user study comparing the perception of light in a real-world low-light scene to a VR simulation. Our results show that the proposed combination of simulated visual effects is well received by users and also indicate that an individual adaptation is necessary, because perception of light is highly subjective.

Nowadays, point clouds are the standard product when capturing reality independent of scale and measurement technique. Especially, Dense Image Matching (DIM) and Laser Scanning (LS) are state of the art capturing methods for a great variety of applications producing detailed point clouds up to billions of points. In-depth analysis of such huge point clouds typically requires sophisticated spatial indexing structures to support potentially long-lasting automated non-interactive processing tasks like feature extraction, semantic labelling, surface generation, and the like. Nevertheless, a visual inspection of the point data is often necessary to obtain an impression of the scene, roughly check for completeness, quality, and outlier rates of the captured data in advance. Also intermediate processing results, containing additional per-point computed attributes, may require visual analyses to draw conclusions or to parameterize further processing. Over the last decades a variety of commercial, free, and open source viewers have been developed that can visualise huge point clouds and colorize them based on available attributes. However, they have either a poor loading and navigation performance, visualize only a subset of the points, or require the creation of spatial indexing structures in advance. In this paper, we evaluate a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time without the need of time consuming hierarchical acceleration structure generation. In combination with our multi-threaded LAS and LAZ loaders, we achieve load performance of up to 20 million points per second, display points already while loading, support flexible switching between different attributes, and rendering up to one billion points with visually appealing navigation behaviour. Furthermore, loading times of different data sets for different open source and commercial software packages are analysed.

In this paper we propose two real-time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.4 milliseconds per frame to execute (for typical portrait shots). This makes them a practical option for real-time production scenarios. Current state-of-the-art, real-time approaches simulate subsurface light transport by approximating the radially symmetric non-separable diffusion kernel with a sum of separable Gaussians, which requires multiple (up to twelve) 1D convolutions. In this work we relax the requirement of radial symmetry to approximate a 2D diffuse reflectance profile by a single separable kernel. We first show that low-rank approximations based on matrix factorization outperform previous approaches, but they still need several passes to get good results. To solve this, we present two different separable models: a) kernel preintegration, which yields a high-quality diffusion simulation; and b) an artist friendly model, which offers an attractive trade-off between physical accuracy and artistic control. Both reduce rendering subsurface scattering to just two 1D convolutions, while delivering results comparable to techniques with higher cost. Using importance-sampling and our stochastic sampling strategies, that rotate part of the kernel, only seven samples per pixel are required, even for high resolution imaging or close ups where our separable kernels might yield artifacts. We additionally show that separable profiles built with only two 1D Gaussians are able to perceptually match Monte 2 Jimenez et al. / Separable Subsurface Scattering: Expanded Technical Report 0 5 10 15 20 10−4 10−2 100 Singular values M ag ni tu de Channels

Efficient rendering of multiple views can be a critical performance factor for real-time rendering applications. Generating more than one view multiplies the amount of rendered geometry, which can cause a huge performance impact. Minimizing that impact has been a target of previous research and GPU manufacturers, who have started to equip devices with dedicated acceleration units. However, vendor-specific acceleration is not the only option to increase multi-view rendering (MVR) performance. Available graphics API features, shader stages and optimizations can be exploited for improved MVR performance, while generally offering more versatile pipeline configurations, including the preservation of custom tessellation and geometry shaders. In this paper, we present an exhaustive evaluation of MVR pipelines available on modern GPUs. We provide a detailed analysis of previous techniques, hardware-accelerated MVR and propose a novel method, leading to the creation of an MVR catalogue. Our analyses cover three distinct applications to help gain clarity on overall MVR performance characteristics. Our interpretation of the observed results provides a guideline for selecting the most appropriate one for various use cases on different GPU architectures. CCS Concepts • Computing methodologies → Rasterization; Visibility; Virtual reality;