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.

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.

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.

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.

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.

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.