Abstract Rendering diffuse global illumination in real‐time is often approximated by pre‐computing and storing irradiance in a 3D grid of probes. As long as most of the scene remains static, probes approximate irradiance for all surfaces immersed in the irradiance volume, including novel dynamic objects. This approach, however, suffers from aliasing artifacts and high memory consumption. We propose Neural Irradiance Volume (NIV), a neural‐based technique that allows accurate real‐time rendering of diffuse global illumination via a compact pre‐computed model, overcoming the limitations of traditional probe‐based methods, such as the expensive memory footprint, aliasing artifacts, and scene‐specific heuristics. The key insight is that neural compression creates an adaptive and amortized representation of irradiance, circumventing the cubic scaling of grid‐based methods. Our superior memory‐scaling improves quality by at least 10x at the same memory budget, and enables a straightforward representation of higher‐dimensional irradiance fields, allowing rendering of time‐varying or dynamic effects without requiring additional computation at runtime. Unlike other neural rendering techniques, our method works within strict realtime constraints, providing fast inference (around 1 ms per frame on consumer GPUs at full HD resolution), reduced memory usage (1–5 MB for medium‐sized scenes), and only requires a G‐buffer as input, without expensive ray tracing or denoising.

Abstract Synthesising high‐quality views of dynamic scenes via path tracing is prohibitively expensive. Although caching offline‐quality global illumination in neural networks alleviates this issue, existing neural view synthesis methods are limited to mainly static scenes, have low inference performance or do not integrate well with existing rendering paradigms. We propose a novel neural method that is able to capture a dynamic light field, renders at real‐time frame rates at 1920×1080 resolution and integrates seamlessly with Monte Carlo ray tracing frameworks. We demonstrate how a combination of spatial, temporal and a novel surface‐space encoding are each effective at capturing different kinds of spatio‐temporal signals. Together with a compact fully‐fused neural network and architectural improvements, we achieve a twenty‐fold increase in network inference speed compared to related methods at equal or better quality. Our approach is suitable for providing offline‐quality real‐time rendering in a variety of scenarios, such as free‐viewpoint video, interactive multi‐view rendering, or streaming rendering. Finally, our work can be integrated into other rendering paradigms, e.g., providing a dynamic background for interactive scenarios where the foreground is rendered with traditional methods.

In this paper we present a volumetric lighting model, which simulates scattering as well as shadowing in order to generate high quality volume renderings. By approximating light transport in inhomogeneous participating media, we are able to come up with an efficient GPU implementation, in order to achieve the desired effects at interactive frame rates. Moreover, in many cases the frame rates are even higher as those achieved with conventional gradient-based shading. To evaluate the impact of the proposed illumination model on the spatial comprehension of volumetric objects, we have conducted a user study, in which the participants had to perform depth perception tasks. The results of this study show, that depth perception is significantly improved when comparing our illumination model to conventional gradient-based volume shading. Additionally, since our volumetric illumination model is not based on gradient calculation, it is also less sensitive to noise and therefore also applicable to imaging modalities incorporating a higher degree of noise, as for instance magnet resonance tomography or 3D ultrasound.

Myocardial perfusion imaging with single photon emission computed tomography (SPECT) is an established method for the detection and evaluation of coronary artery disease (CAD). State-of-the-art SPECT scanners yield a large number of regional parameters of the left-ventricular myocardium (e.g., blood supply at rest and during stress, wall thickness, and wall thickening during heart contraction) that all need to be assessed by the physician. Today, the individual parameters of this multivariate data set are displayed as stacks of 2D slices, bull's eye plots, or, more recently, surfaces in 3D, which depict the left-ventricular wall. In all these visualizations, the data sets are displayed side-by-side rather than in an integrated manner, such that the multivariate data have to be examined sequentially and need to be fused mentally. This is time consuming and error-prone. In this paper we present an interactive 3D glyph visualization, which enables an effective integrated visualization of the multivariate data. Results from semiotic theory are used to optimize the mapping of different variables to glyph properties. This facilitates an improved perception of important information and thus an accelerated diagnosis. The 3D glyphs are linked to the established 2D views, which permit a more detailed inspection, and to relevant meta-information such as known stenoses of coronary vessels supplying the myocardial region. Our method has demonstrated its potential for clinical routine use in real application scenarios assessed by nuclear physicians.

The detection of the types of local surface form deviations is a major step in the automated quality assessment of car body parts during the manufacturing process. In previous studies we compared the performance of different soft computing techniques for this purpose. We achieved promising results with regard to classification accuracy and interpretability of rule bases, even though the dataset was rather small, high dimensional and unbalanced. In this paper we reconsider the collection of training examples and their assignment to defect types by the quality experts. We attempt to minimize the uncertainty of the quality experts' subjective and error-prone labelling in order to achieve a higher reliability of the defect detection. We show that refined and more accurate classification models can be built on the basis of a preprocessed training set that is more consistent. Using a partially supervised learning strategy we can report improvements in classification accuracy.

In fuzzy clustering soft cluster partitions are formed based on the similarity of data points to the respective cluster prototypes. Similarity is defined in terms of simultaneous closeness regarding all attributes. In some applications the values of many attributes have been measured, but a natural clustering, if it exists, occurs within a (small) subset of attributes. The remaining dimensions can be considered irrelevant. They can obscure an existing grouping and make it harder to discover the cluster structure. In probabilistic fuzzy clustering irrelevant attributes can lead to coincidental cluster centers in the worst case. We study this effect in detail as well as the robustness of different similarity functions and their possible parameterizations against irrelevant input dimensions. Empirical evidence is given for the different properties of the membership functions