Pixel art is an art style that uses low-resolution raster images with few colors. This style was ubiquitous in video games and computer programs in the late 20th century before high-resolution screens and powerful computers were widely available. In order to remaster these old games, or to use pixel art as an intermediate format to create vector images, conversion to vector formats such as Scalable Vector Graphics (SVG) is desirable. Existing tools have several limitations, notably a lack of support for gradients. We introduce a new method that expands upon an existing pixel art vectorization solution to add support for gradients, while maintaining compatibility with SVG and enabling real time editing for ease of use. Our method works by blurring parts of the image and then using various techniques to improve the quality of the output. We test our solution by creating example images with our tool’s automatic generation capabilities and modifying them with the available controls. We conclude that our method is useful for imitating smooth lighting and real-world gradients, enabling artists to create more expressive vector versions of pixel art.

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 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.

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.

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

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 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.

Traditional bilateral filters, effective in 2D image processing, often fail to account for the 3D structure of meshes, leading to artifacts in texture filtering. This thesis introduces On-Mesh Bilateral Filtering, a novel method that adapts the bilateral filter to work with non-contiguous texture mappings by incorporating 3D spatial distances and face adjacency information into the filtering process. The On-Mesh Bilateral Filter combines mesh surface sampling techniques with heat geodesic distance calculations to create a geometry-aware kernel that achieves more accurate and context-sensitive smoothing operations, respecting both the mesh topology and texture space properties. This paper hopes to encourage further research in the area of geometry-aware texture filters.

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.