To incentivize solar panel adaptation, a multitude of solutions are actively being researched. Among these solutions are colored solar cells, using coatings or filters to open the door for more architectural expression. In order to help architects explore these possibilities, it is necessary to enable flexible, fast, and realistic visualization. This work explores the modeling of color-coated photovoltaic cells using a physically based bidirectional reflectance distribution function (BRDF), with a focus on the pyramidically textured structures embedded inside many of these cells. The BRDF is analytically derived, modeling light interactions as recursively specular multiple-scattering. The model is parametric, characterizing the surface using its pyramid density and pyramid slant angle, making it generally applicable to homogeneous pyramidic surfaces with uniform pyramid heights. Evaluation indicates the model closely approximates the behavior of generated references at steep viewing angles. Clear avenues of improvement, including corrections at shallow angles, are discussed within the context of future work.

3D Gaussian splatting (3DGS) is an appealing implementation of novel view synthesis, with fast training and render times compared to related methods. However, per-frame sorting and front-to-back alpha compositing lead to a significant decline in performance for scenes with a high number of Gaussians. In particular, the alpha compositing step in the original implementation inefficiently handles overdraw. Therefore, dense clusters of Gaussians observed at a distance significantly increase frame times.


In this thesis, we propose a stochastic rendering approach that significantly improves performance in large scenes, by integrating principles from stochastic transparency and compute-based point cloud rendering. Each frame, we first compute a weight for each Gaussian based on its screen footprint and opacity. These weights are then used to construct an alias table on the GPU. We then splat points, each sampled from a Gaussian selected in proportion to its weight using the alias table, onto an interleaved depth-and-color buffer. By using an atomic operation, we ensure that the points closest to the camera are kept without sorting. To construct the final image, we average multiple samples per pixel, obtained across multiple point cloud passes. Although our approach introduces noise at low sample counts and minor artifacts due to the independent and identically distributed nature of the sampling, these effects can be mitigated using temporal anti-aliasing and rejection sampling respectively.

Our evaluations indicate that the render time of our method depends primarily on the density of Gaussians near the camera, rather than the total number in the scene. This allows our method to efficiently handle large-scale scenes.

Augmented Reality (AR) offers significant potential to enhance surgical procedures by overlaying critical digital information directly onto the surgeon's view, improving spatial awareness and precision. Accurate real-time tracking of surgical instruments is fundamental to the success of AR navigation systems. However, implementing established tracking modalities, such as infrared (IR) tracking using retro-reflective markers, on newer Head-Mounted Displays (HMDs) like the Magic Leap 2 presents unique challenges due to its specific sensor configuration, notably the lack of accessible IR intensity data from its Time-of-Flight (ToF) sensor and the presence of IR-cut filters on its World Cameras.

This thesis addresses these challenges through the development, implementation, and evaluation of ISITLeap, a novel system designed specifically for IR instrument tracking on the Magic Leap 2 platform. The system employs a stereo vision approach, utilizing the Magic Leap 2's built-in World Cameras configured with short exposure times, synergistically combined with a custom-designed external 760 nm IR illumination module to detect standard passive retro-reflective markers. A comprehensive software pipeline was developed, incorporating image undistortion adapted for the Magic Leap 2's equidistant projection model, marker detection involving binarization, connected components analysis, and area filtering, 3D marker reconstruction using stereo triangulation with empirically derived coordinate system corrections to reconcile Magic Leap 2 SDK data with geometric requirements, and finally, 6-DoF tool pose estimation via point-based registration. Crucially, the entire software pipeline, from image acquisition to pose estimation, operates directly on the Magic Leap 2 headset, requiring no external computing hardware.

Experimental validation successfully demonstrated the feasibility of the ISITLeap system for real-time tracking. Static evaluations characterized the system's performance, identifying an optimal working range of approximately 25-50 cm. Dynamic tracking experiments, evaluated against an NDI optical tracker using an ArUco marker as a common reference frame, yielded an overall Mean Absolute Error (MAE) of approximately 7 mm. Crucially, error analysis revealed that the measured accuracy was significantly influenced by limitations inherent in the ArUco marker-based evaluation methodology, likely masking the intrinsic performance capabilities of the ISITLeap core tracking system.

This work presents a complete, functional hardware and software solution tailored to overcome the specific sensor constraints of the Magic Leap 2, enabling IR marker tracking on this platform. It provides a crucial performance benchmark under the defined evaluation conditions and demonstrates a viable technical pathway for developing precise AR surgical navigation applications for the Magic Leap 2 headset. While the achieved referenced accuracy highlights the need for improved evaluation techniques and further system refinement (e.g., via dynamic filtering and optimized hardware), ISITLeap establishes a foundational system for future advancements in Magic Leap 2-based AR surgical guidance.

This thesis presents a computational framework aimed at enabling the analysis and modeling of boomerangs from example shapes. The goal is to provide a systematic and data-driven tool for boomerang design based on real-world geometries. A key challenge in this context is establishing accurate shape correspondences between handcrafted, asymmetric, and aerodynamically functional boomerangs.
To address this, the proposed pipeline integrates multiple components: landmark-based pre-alignment, boundary extraction using alpha shapes, curve parameterization, and Least Squares Conformal Mapping (LSCM) to compute surface correspondences. Building on these correspondences, the framework further incorporates principal component analysis (PCA) and Free-Form Deformation (FFD) to enable the generation of new shapes.
Experimental results show that the method achieves low Hausdorff and Chamfer distances and has been evaluated using area- and shear-based distortion metrics. Nonetheless, some localized inaccuracies - particularly near high-curvature regions - highlight areas for improvement in boundary handling and local control. Additional limitations include the reliance on manual landmark selection, the linearity of PCA, and the sensitivity of the pipeline to alpha shape parameters.
By combining elements of computational geometry with principles of aerodynamic design, this research bridges the gap between empirical craftsmanship and formal shape analysis. The resulting methodology not only enables the comparison and modeling of boomerangs but also lays the groundwork for future tools in boomerang shape exploration and design.

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.

Lag in Virtual Reality can be devastating to the enjoyment of participants. This paper reduces the lag in a physics system by implementing a subset of the architecture proposed by Loren Peitso and Don Brutzman. This subset contains an event based physics system together with a networking system. The collision system of the physics system has been heavily reduced in size, as to speedup development of the architecture.

This architecture seems promising and handles predictable cases with almost no noticeable lag. This architecture does not handle external events well, as the lag is noticeable. The `healing' process for unexpected external events should be researched further to make this architecture viable.

Virtual Reality's uniquely immersive nature can facilitate skill transfer between an instructor and a student. Through the use of statically transparent ghost instructors that are superimposed on the student avatar, the student can learn skills by observing the ghost instructor's movements from a first-person perspective.

This paper aims to investigate the effect that changing the transparency values of the ghost avatar based on student performance has on learning and retaining skills. A small-scale user study has been conducted to contrast these dynamically and statically transparent ghost instructors. While the group taught by statically transparent ghost instructors displayed better performance improvement between training trials, the performance drop between the training and test trials was much smaller for the group taught by dynamically transparent ghost instructors.

Effective text input in VR is an ongoing problem with no best-agreed-upon solution. One effective solution for this is a word-gesture keyboard implemented in VR. These keyboards work by matching the user-drawn gestures to predefined gestures of all words that connect consecutive letters of the word with straight lines. We investigate matching the user input with gestures comprising of Bézier curves. We found a small improvement in writing speed and a reduction in errors while writing. The method of using complex curves for matching gestures seems promising with many possible ways for further extension.

Social uses of virtual reality, such as collaborative virtual environments (CVEs), are showing significant increases in general adoption. In these CVEs, it is desirable for users to feel a high level of presence, which can increase collaboration effectiveness. This study investigated the effect of facial realism on presence in CVEs. Facial realism was implemented through including eye- and mouth-tracked facial expressions in an avatar representation. A controlled within-dyad experiment was performed, consisting of a dyadic interaction between participants in a CVE. We found no significant difference between a Static Face condition and either Eye Tracked or Full Tracked (eye- and mouth) facial expression conditions. Some individual items on the questionnaire were significant or marginally significant, suggesting some positive impact on the assessment of partner reactions in the Full Tracked condition, compared to Static Face. Participants also reported a lower feeling of correspondence between virtual experiences and their physical body in the Full Tracked and Eye Tracked conditions. Some evidence has been found that this experiment warrants repetition with changes discussed in this paper, which may yield significant differences with a higher sample size.

The application of Augmented Reality (AR) technology in neurosurgery is becoming increas- ingly widespread, especially in External Ventricular Drainage (EVD) procedures, where achiev- ing efficient and precise registration remains a key challenge. Traditional marker-based registra- tion methods are often complex and time-consuming, making them unsuitable for emergency surgical situations. This paper presents a markerless automatic registration method based on HoloLens 2, using the Dlib library to automatically detect facial landmarks, and perform- ing point cloud registration based on these features. We designed four sets of experiments: first, to verify the feasibility of the triangle-based registration algorithm; second, to assess the accuracy and stability of Dlib’s feature point extraction on HoloLens; third, to validate the registration accuracy, including the impact of skin displacement; and finally, to evaluate the accuracy of the insertion path in a simulated EVD procedure. The experimental results show that this method simplifies the registration process and demonstrates advantages in terms of registration accuracy and speed, effectively meeting the real-time and precision requirements of emergency surgeries. This study provides an efficient and reliable AR navigation solution for EVD procedures, with promising prospects for further research.

In this thesis, we investigate a CPU-based ray tracer tailored for semiconductor structures. This ray tracer addresses the need for high-quality visualization in environments where GPU acceleration is unavailable. Our primary objective is to achieve interactive visualization performance with quality levels on par with a basic triangle rasterizer employing a flat-shaded lighting model. The research focuses on identifying optimal BVH configurations and developing efficient Oriented Bounding Box (OBB) ray tracing methods. By leveraging OBB clustering and pre-cached ray directions, we aim to mitigate the computational overhead associated with OBB tracing. The result is a prototype that demonstrates the feasibility of CPU-based ray tracing for real-time applications. This work contributes to the broader discourse on rendering techniques in GPU-limited scenarios, offering valuable insights and laying the groundwork for future research and development.

Creating photorealistic images is one of the ultimate goals of computer graphics. Previous work has shown that a material's microstructure plays a crucial role when trying to achieve photorealism. This is because a material's appearance depends on the roughness of its microstructure. Due to this dependence, effects such as masking and shadowing have to be taken into account, as these are capable of altering the effective reflectance of a material. Render engines typically use a mathematical expression, known as a visibility function, that aims to calculate the impact of these effects. However, even the best visibility function known is still an approximation; an exact solution doesn't exist. In order to evaluate the accuracy of visibility functions, an algorithm can be created that computes the correct output, such that the output of a given visibility function can be compared against it. Such an algorithm can be one of two types: approximative or exact. In this paper, we show that approximative algorithms are very capable and come close to their exact counterparts. However, there is still a non-negligible difference between them, meaning they aren't suitable for applications that demand very high levels of accuracy.

This research proposes a new algorithm for mapping Normal Distribution Functions to Heightfields in order to answer its research question: ”Given an NDF, how can we generate a corresponding Heightfield using simple optimization algorithms?”. This research is important, as it helps us to gain a better understanding of how limited statistics-based representations of 3D surfaces are. To this end, we have produced an algorithm using the Simulated Annealing optimization technique, a technique that randomly explores possible solutions of a problem until it finds the optimal solution. The algorithm begins with a flat Heightfield (a 2D representation of a surface that shows the relative altitude of a discrete plane of points), iteratively changes points on the Heightfield and compares its measured NDF (Normal Distribution Function, a function to denote the area distribution along a given direction in a Heightfield) to the target NDF that we want to map. Once the target NDF is reached, or once the pre-determined number of iterations has been reached, the algorithm concludes and the NDF-to-Heightfield mapping has been completed. Three different variations are tried, one na¨ıve implementation which changes points on the Heightfield completely at random, another where the angle of the normal vector of a random surrounding facet of a chosen point is used as guidance for randomization, and finally one where this angle is guided using the relative position of the chosen point to the centre of the Heightfield. The conclusion the proposed algorithm provides is that, while possible, the process of mapping NDFs to Heightfields is a costly and complex operation, and leaves a lot of room for ambiguity. While the research cannot provide a case of an exact match of a target NDF and measured NDF of a Heightfield created through the algorithm, we do show it is without a doubt possible given time.

The micro detail on surfaces can have a profound effect on how rays bounce of it. In ray tracing, this micro-surface is normally approximated with statistical models. We wish to figure out what the effect of normal interpolation and Phong tessellation is on how rays reflect on the surface. This has been Done by creating a ray tracers from scratch, that uses height fields, with normal interpolation and Phong tessellation. This ray tracer has then been used to render 3 different micro-surfaces, which were compared in order to understand the difference. The conclusion of this research was that both smoothing techniques had little to no effect on the reflection of rays on micro-surfaces with a high triangle density. However on micro-surfaces with a low triangle density, there was a much more pronounced difference with the smoothing techniques. A second conclusion was made which was that rougher surfaces have a more spread out distribution of rays than smoother surfaces.

Bidirectional Reflectance Distribution Functions, BRDFs, describe the reflectance of light on a ma-terial, and are widely used in computer graphics to render materials. Acquiring a full measured BRDF can be costly and time consuming, so this research aims to answer the question ”How can we approx-imate a full BRDF from a single slice (in-plane BRDF)?”. Outlined in this paper is an algorithm that uses solids of revolution to approximate a full BRDF from a single slice. The algorithm finds sub-curves of the slice, creates solids of revolution for each, normalizes the data, and merges the solids while removing overlapping data. The resulting solid, described by a list of points using a Carte-sian coordinate system, represents the full, three-dimensional BRDF.

Rendering accurate water reflections is crucial for achieving realism in computer graphics. Their integration with VR and AR technologies can further elevate experiences in sectors like education, engineering, and medicine. Even though various monoscopic reflection techniques exist they either demand substantial computational resources or cannot be straightforwardly adapted to stereoscopic media, leaving a big hole in research concerning stereo-aware reflections and their applications. This thesis researches and analyses the different techniques for water reflections. Further, it makes significant strides in the development of stereo-consistent real-time water reflections for XR platforms. This is achieved by combining implicit functions and bounding volume hierarchies (BVH) into a unified system called Adaptive Hierarchical Signed Distance Fields Reflections (AH-SDFR). It was found that AH-SDF outperforms leading methods such as Hierarchical Screen Space Reflections (HSSR) and Reflection Probes in both visual quality and performance, and as a result, it offers a promising solution for XR applications. However, challenges persist, mainly due to the large memory demands of intricate 3D scenes and perceptual errors from inaccuracies in volume textures.

Multimodal imaging is used by conservators and scientists to study the composition of paintings. To aid the combined analysis of these scans, such images must first be aligned. Rather than proposing a new domain-specific descriptor, we explore and evaluate how existing feature descriptors from related fields can improve the performance of feature-based painting scan registration. We benchmark these descriptors on pixel-precise, manually aligned scans of “Girl with a Pearl Earring” by Johannes Vermeer (c. 1665, Mauritshuis) and of “18th Century Portrait of a Woman”. As a baseline we compare against the well-established classical SIFT descriptor. We consider two recent descriptors: the handcrafted multimodal MFD descriptor, and the learned unimodal SuperPoint descriptor. Experiments show that SuperPoint starkly increases description matching accuracy by 40% for modalities with little modality-specific artefacts. Further, performing craquelure segmentation and using the MFD descriptor results in significant description matching accuracy improvements for modalities with many modality-specific artefacts.

Image registration is the process that overlays two or more images from different sources taken at different times and angles. Art conservators take various scans of paintings and then register them against the original in order to learn more about the working style of the artist, materials used and physical changes throughout time. This paper describes how scale space theory could be applied with a variant of cross-correlation to find the right scale at which to register the input images. From there, the standard state-of-the-art approach is used to register the images. This added step allows automatically registering a wider range of images.

This research investigates the efficacy and reliability of geometric matching for the specific case of aligning non-exact copies of artistic works with the original from which they were derived. The purpose of which is to provide a foundation for comparison in any further analysis conducted by conservators and art historians. An overview of the image alignment field as a whole is provided as well as a discussion of the particulars of aligning artworks. A variety of demonstrative results indicate the strengths and weaknesses of the technique in this domain and are accompanied by a discussion of the headline conclusions.

The current state-of-the-art image alignment techniques and their input parameters are often unintuitive to those without the required background knowledge.
This work aims to provide users with a graphical user interface through which they can intuitively influence the parameters and results of the algorithm by excluding certain areas from the images.
The application is also capable of automatically optimizing the parameters of the underlying registration algorithm.
Experimental results indicate that the automatic parameter optimization results in alignments at least as good as the default parameters, but that the introduced user interaction does not provide any major benefits.