Simulating how crowds move through complex environments is essential for applications in urban planning, robotics, gaming, and safety analysis. However, many real-world spaces—such as multi-story buildings, staircases, and layered architectural designs—are too complex for traditional 2D or CPU-based crowd simulation methods, which often oversimplify geometry or become computationally infeasible. This thesis introduces a fully GPU-accelerated crowd simulation framework that efficiently handles complex, multi-layered 3D environments. Building on the Continuum Crowds algorithm, our method extracts walkable surfaces at different height levels, identifies vertical obstructions, and enables real-time navigation for thousands of agents. The system avoids common artifacts such as unrealistic wall clipping and enables realistic movement across layers. In performance benchmarks, our GPU implementation achieves a speedup between 5× and 1000× over the original CPU-based method, depending on scenario complexity, demonstrating strong scalability and consistent real-time performance. This makes it particularly valuable in domains such as robotics, where anticipating pedestrian flow is crucial for safe and intelligent robot navigation in dynamic environments.

The efficient rendering of scenes with many light sources remains one of the most challenging problems in real-time ray tracing. As the complexity of virtual environments continues to increase, with some scenes containing thousands of light sources, traditional Monte Carlo methods struggle to achieve acceptable noise levels under real-time constraints. This paper introduces a novel approach that combines Reservoir-based Spatiotemporal Importance Resampling (ReSTIR) with a specialised bounding volume hierarchy (BVH) structure to enhance light candidate generation for scenes with many light sources. The BVH-assisted candidate generation is tested on multiple scenes, resulting in a significant decrease in image noise levels measured with the root mean squared metric (RMSE), along with improved visual quality from the first frame onward, especially in scenes with numerous light sources illuminating local areas.

Lens flares occur when a bright light source induces light to travel through an optical lens system via unintended paths, reaching the sensor at an undesired location. Although arising from the imperfection of lens systems, flares are widely used in the visual entertainment industry for artistic purposes and to increase perceived brightness.

Research on physically accurate lens flare rendering has come far and produced convincing results by considering the inner construction of the lens and how light interacts with it. However, obtaining a specific flare signature through these algorithms requires lens design expertise that the typical artist does not possess.

This thesis demonstrates how to obtain optical lens systems that achieve a desired flare effect, without requiring prior knowledge of lens construction. We achieve this through simplified controls and evolutionary algorithms. With this method, existing lenses can be tweaked, or even new ones can be built from scratch. The process abstracts the lens and algorithmic parameters, making these rendering algorithms accessible to a wider range of users.

Realistic image synthesis in computer graphics relies heavily on global illumination (GI) to simulate indirect lighting. Computing GI remains one of the most computationally demanding tasks. Traditional approaches like Instant Radiosity (photon mapping) generate virtual point lights (VPLs) to approximate indirect lighting, but become inefficient in complex scenes due to reliance on uniform light sampling. As the number of VPLs grows, this method leads to increased noise and reduced performance.
In this paper we explore the integration of ReSTIR DI (Reservoir-based Spatiotemporal Importance Resampling for Direct Illumination) with photon-mapped VPLs to improve the efficiency and quality of global illumination. By leveraging ReSTIR’s ability to reuse and resample light paths spatially and temporally, the aim is to address the scalability issues of uniform sampling and enable practical, real-time GI rendering.
The approach is evaluated using three test scenes of varying complexity: Cornell Box, Sahur, and a detailed living room scene. Performance metrics include root mean squared error (RMSE) comparison against a reference and average frame times across different sampling methods. Results demonstrate that ReSTIR with photon mapping achieves significantly lower RMSE values and faster convergence compared to uniform sampling, with an average RMSE improvement of 57.4% across all tested scenes. The method shows particularly strong improvements in visually complex scenes, with ReSTIR consistently producing the cleanest results at 1 sample per pixel. However, the integration within the current implementation introduces computational overhead, with frame times increasing by 4-7 times compared to uniform sampling, and occasional blotting artifacts in regions with high VPL density. Despite these limitations, the work establishes that ReSTIR DI can be successfully extended to handle photon-mapped global illumination, providing a foundation for improving VPL-based rendering efficiency while maintaining visual quality.

Spectral Monte Carlo rendering stands as the gold standard for accurately simulating complex optical phenomena, such as fluorescence and chromatic dispersion, but typically exhibits slow convergence behavior due to challenges in sampling the wavelength domain. Convergence is even slower for scenes with complex spectral distributions, and hence a robust method for sampling the wavelength domain is crucial for better performance. To address this challenge, we propose a preprocessing step consisting of optimizing a set of distributions specifically for wavelength sampling, and we investigate whether this approach can achieve a lower perceptual error than traditional sampling techniques. Our evaluation indicates that our method can significantly reduce perceptual error in single-emitter scenes featuring a complex spectral power distribution (SPD), when compared to uniform sampling, without incurring any additional overhead during rendering.

This thesis addresses the challenge of initial frame noise in real-time ray tracing when using ReSTIR. We propose and evaluate an approach that integrates a normal- aware hash grid for precomputed reservoir caching to improve direct illumination. The research investigates how reservoir caching enhances visual quality alongside ReSTIR and analyzes the associated trade-offs in memory usage and performance. Our con- tribution includes the implementation and analysis of this caching strategy, assessing its impact on visual fidelity across diverse scenes. Although the method significantly reduces noise and improves initial sampling convergence, it can introduce visible grid artifacts in scenes with many light overlaps. Furthermore, this approach incurs notable memory overhead and increased frame times. This work demonstrates the potential of normal-aware hash grids for ReSTIR improvements, providing a proof-of-concept algorithm for stable, high-quality initial samples.

In this paper, we propose techniques to increase the effective resolution of a pathtracer without changing the resolution of the screen for which it is rendered. These make use of the fact that displays are made up of pixels, each of which is made up of three distinct subpixels. By setting luminance values for subpixels rather than colour values for a full pixel, we can create a higher fidelity image. This is already used in rendering text, but has not yet been applied to a pathtracer. It is done in three distinct ways; firstly we send three rays per pixel, each centred at a subpixel to obtain a value for every subpixel, the second method is using a filter used for downscaling text. Lastly, we implement a technique that makes use of the aforementioned filter without increasing the number of samples needed. By sampling a random subpixels each frame for each pixel and combining neighbouring pixels proportional to the filter. We find that these methods are quite effective at the goal of increasing perceived resolution in high-contrast scenes.

In pixel art, texturing is the process of adding detail to an object to make it resemble a real material. While texturing is crucial in creating high-quality pixel art, it is an arduous process that is time consuming for artists. We present an algorithm that automates texturing by arranging elements throughout an input image in a way that implies a feature of the reproduced object, outlines its tridimensional shape, and respects the original shading. We additionally present heuristics on how to determine the color of a feature drawn over existing pixel art, and a method to create vector fields from user brush strokes. Aside from easing artists' work, this algorithm is an important step towards the procedural generation of pixel art. Quality is demonstrated by comparing results to manually authored textures.

This paper investigates how standard 3D reconstruction techniques can be adapted to work with orthographic projections of pixel art images. Reconstructing 3D models from 2D images is typically done with real world objects. However, little work has explored this problem in the context of pixel art, which has a lower resolution, and uses stylistic colors and shading, making it a problem that requires different or adapted techniques. We implement three reconstruction algorithms based on some common practices in the field: silhouette-based intersection, spatial carving using color consistency, and gradient-based depth estimation. Results show that silhouette intersection is an effective method for simple models, but fails to capture concave regions. Spatial carving addresses this limitation, although it also fails in some use cases. An adapted depth estimation technique is also used, which most accurately captures these regions, although it does not fill in the model as well. We implement a custom Blender plugin to support user annotations and improve accuracy in some ambiguous areas. We conclude that a hybrid approach, where silhouette intersection is combined with depth estimation, gives the most accurate results, and we suggest future work that can be done on the topic, including better color merging principles, adding custom viewpoint orientations, adding support for multiple object reconstruction and using perspective projections.

Pixel art can suffer from perceptual artifacts, such as banding and pillow-shading, which result from poor pixel placement and weaken visual quality. Banding occurs when two adjacent pixel segments of different colors align their endpoints along a shared axis. Pillow-shading is a pronounced form of banding, characterized by concentric strips that perfectly follow the object's outline. Prior methods for fixing artifacts in pixel art rely heavily on auxiliary reference images and do not address banding or pillow-shading. We present a semi-automated method that corrects both artifacts using established artistic guidelines and without relying on references. It supports user annotations to resolve ambiguities when multiple corrections are possible and to help preserve the artistic intent. We tested our software tool, Pixel Fixer, on several pixel artworks affected by banding or pillow-shading. Using an error metric that counts pairs of banded pixel segments, we observed consistent reductions across all inputs.
Related dataset 4TU.ResearchData: https://doi.org/10.4121/11446578-c1f3-474c-9cc4-6978a79b675b

Pixel art relies on carefully constructed color ramps to simulate shading and depth within limited palettes. However, editing these ramps remains a tedious and error-prone manual process. This research introduces a semi-automatic tool that supports the detection and modification of color ramps in pixel art. The system builds a graph to model relationships between image colors based on perceptual similarity and spatial adjacency, then extracts and validates color ramps using customizable criteria. Afterwards, if a user edits a color, changes propagate consistently along the defined ramps, preserving their visual structure. The method streamlines palette editing while respecting artistic intent, offering both automation and control.

3D rendering is traditionally based on a tristimulus approximation, where all light, color, and spectral distributions are represented using three (RGB) values. For enhanced physical accuracy, spectral rendering algorithms can be employed. However, these methods are typically more computationally expensive and require scene and material data measured from the real world. With few exceptions, spectral rendering remains confined to academic research, with limited adoption in production pipelines due to the many challenges it poses. In this dissertation, we identify several of these challenges and propose practical solutions to each.

Fiber tracking enables the in-vivo reconstruction of white matter pathways in the brain and has significant potential in clinical workflows such as neurosurgical planning. However, its broader clinical adoption remains limited due to the high degree of uncertainty that arises throughout the processing pipeline, from diffusionMRI acquisition to modeling, tracking, and visualization. These uncertainties are rarely communicated in current clinical visualizations, which often present results as definitive, potentially misleading clinicians and affecting critical decisions.
This thesis explores the integration of fiber tracking uncertainty visualization into neurosurgical workflow, aiming to enhance the interpretability and transparency of the results in a clinical context. A key challenge lies in balancing computational complexity with clear representation, while ensuring the solutions remain aligned with clinical workflows.
To address this, we introduce interactive and computationally efficient visualization approaches that represent uncertainties and support clinicians in understanding how fiber tracking results may vary with inherent uncertainties. These techniques are evaluated through collaborations with medical experts and incorporated into decisionmaking studies to assess their practical relevance.
The contributions presented in this thesis advance the integration of uncertainty into clinical fiber tracking visualization and highlight how embracing uncertainty, rather than ignoring it, can lead to safer and more informed clinical decisions.

This dissertation investigates the use of deep-learning methods for user-guided content creation, manipulation, and exploration. It illustrates the potential of neural techniques to support working with large data collections and we illustrate our solutions through several applications. Regarding content generation, we propose an algorithm to produce material representations from a single image. We illustrate content manipulation with an approach to perform perceptually plausible interpolation and examine exploration in the context of interactive retrieval. For the latter, we show that features spaces are of high relevance to organize data and show the generality of this concept by proposing novel exploration methods for image and music collections.

Ray tracing has experienced increasing adoption in various spaces of computer graphics. The ReSTIR (Reservoir-based Spatiotemporal Importance Resampling) family of techniques has enabled several orders of magnitude speedups in light transport simulation algorithms which rely on ray tracing.

We introduce an extension to WRS (Weighted Reservoir Sampling), a key component of ReSTIR, to reduce the occurrence of duplicate samples in multi-sample reservoirs. Further, we show how samples from multiple reservoirs can be combined in an MIS-style estimator as opposed to resampling from them. Lastly, we combine these two components to compute optimal weights for this estimator in a similar vein to OMIS (Optimal Multiple Importance Sampling).

Our direct lighting proof of concept implementation demonstrates the efficacy of our WRS extension, lowering the variance of ReSTIR, particularly with difficult lighting arrangements. Further, our MIS-style estimator shows a significant improvement compared to ReSTIR. In problem domains where resampling produces a function value for integration, such as path tracing, this comes at no additional cost. Lastly however, optimal weights do not appear to be beneficial for our approach.

The prevalence of partial differential equations (PDEs) in modeling physics and the low speeds of numerical solvers demands more efficient solving methods. For this purpose, machine learning based methods have been proposed, but these are typically discrete, difficult to interpret and require large amounts of ground truth data to train. To address these issues, we propose a novel machine learning based solver that builds upon advancements in Gaussian based reconstruction. Our method represents the solution to a time-dependent target PDE purely in terms of Gaussians, making it completely continuous and meshless. These Gaussians are evolved by means of an autoregressive neural network that is applied to each Gaussian, integrating information from a local neighborhood of Gaussians. This enables the change of position, scale, rotation and value of the Gaussians to model the solution in a particle-like manner. Extensive experiments show the potential for Gaussians to model arbitrary physical phenomena. We also compare our approach to ground truth data and various state of the art methods, which demonstrates that the method performs well on short-term prediction, but does not match the state of the art for long-term prediction.

Unlike traditional blur filters, the bilateral filter exhibits non-linear blur behaviour as its kernel size increases. This atypical blur behaviour makes it challenging to find a good σr . This paper investigates the underlying reasons for this behaviour and proposes methods to align the bilateral filter’s blur scaling linearly with its spatial filter size. Using local frequency analyses to quantify blur levels, we introduce an approach that finds the best σr through iterative search. Results demonstrate that the pro- posed method effectively counters the atypical blur behaviour. However, the proposed method does not perform sufficiently when handling very large kernel sizes. The proposed method can be used to abstract away the σr parameter when seeking linear blur behaviour in the bilateral filter. Further re- search is needed to make it functional for very large kernel sizes.

The bilateral filter is a popular filter in image processing and computer vision. This comes from the fact that it is able to blur images while keeping the structure intact. However, the bilateral filter allows for blurring to happen across edges. This can result in halo-like effects around the edges of structures if both sides are made up of different intensities. In this paper, we propose an extension to the bilateral filter that reduces this phenomenon of blurring across edges. By giving the filter knowledge of the edges beforehand, it is possible to prevent the filter from blurring past them. When we filter a pixel, its surrounding area within the kernel is checked for edges. If a pixel within this area lies on or beyond an edge, its weight for blurring is reduced. As a consequence, pixels that lie past an edge have less influence on blurring. We show that this new edge-aware bilateral filter reduces across-edge blurring compared to the standard bilateral filter. Furthermore, when we allow a bigger range of intensities to mix, the new filter is also able to prevent the filtered image from appearing washed out, unlike the bilateral filter.

This paper introduces the Quadrilateral filter, an advanced extension of the Bilateral and Trilateral filters aimed at addressing limitations in high-gradient regions of images. While the Bilateral filter effectively preserves edges during smoothing, it struggles with intensity variations, leading to blunted image details. The trilateral filter improves upon this by incorporating local plane geometry approximations but assumes linear pixel intensity distributions, limiting its effectiveness. The proposed Quadrilateral filter utilizes curvature-based geometry approximations to enhance noise reduction, contrast preservation, artifact reduction, and image reconstruction by accounting for nonlinear pixel value distributions. The development of this filter represents the main contribution of the paper while exploring whether the established Bilateral and Trilateral filters’ performance can be further improved through curvature-based local geometry approximations. The findings demonstrate improvements in image quality and detail preservation, with broad implications for applications in image de-noising, tone-mapping, multimedia processing, and beyond.

The bilateral filter is an edge-aware image filter. While it has a variety of applications, its naive implementation is quadratic in nature, hindering the ability to efficiently process multi-megapixel images. If performance is needed, like in a real-time setting, an approximation is necessary. Current literature on Fourier series-based approximations does not explore the capabilities of graphics processing units (GPUs) as viable platforms for this computational problem. This paper proposes an approach for implementing such filtering on a GPU by conducting a series of separable convolutions, and also investigates the use of different range kernels. Our adaption of the bilateral filter is found to be more two times faster than readily available solutions, with frame times showing that real-time performance is possible for large spatial kernel sizes and image resolutions.