Low-dimensional datasets, for which each datapoint contains no more than three attributes, are straightforward to visualize with common visualization idioms, such as scatterplots. In order to visualize high-dimensional datasets with potentially thousands of attributes, their dimensionality will need to be reduced. t-SNE is a widely used, state-of-the-art algorithm for non-linear dimensionality reduction. It produces embeddings of the high-dimensional data onto two or three dimensions by using gradient descent to minimize the discrepancy between the probability distributions of the high-dimensional and low-dimensional datapoint similarities, iteratively adjusting the low-dimensional embedding. However, since it is impossible to capture all neighbourhoods over all high-dimensional axes in two or three dimensions, the resulting clustering is merely virtual, only meant to offer an intuition of the dataset's high-dimensional distribution. Our work proposes a number of tools that allow a user to interactively impose constraints based on prior knowledge or assumptions about the dataset and manipulate the underlying probability model with the aim of enhancing an embedding's interpretability, for a better intuition about the data's high-dimensional structure. We demonstrate the effectiveness and limitations of these tools with examples and case studies.

Direct lighting calculation is an essential part of photorealistic rendering. Standard importance sampling techniques converge slowly in scenes where a light source is only visible through small openings as visibility is not considered. This problem is often addressed by manually placing light portals, marking the openings to the light. However, existing portal sampling techniques are not suitable for area lights since the portal is often times larger than the light itself. We present a novel portal sampling technique inspired by shadow volumes, which considers the light geometry to efficiently choose the optimal sampling strategy, depending on the location of the shading point. Our technique is unbiased, robust, applicable to many scenes, and easy to integrate into an existing renderer.

Spectral Monte-Carlo rendering can simulate advanced light phenomena (e.g., dispersion, caustics, or iridescence), but require significantly more samples compared to trichromatic rendering to obtain noise-free images. Therefore, its progressive variant typically exhibits an extreme amount of chromatic noise in early renders. To that end, we propose a two-stage progressive approach. We initially restrict the original wavelength distribution, then slowly relax it. In the process of relaxing the range of wavelengths, all wavelengths that are outside of that restricted range will be propagated. Thereby, we lower variance and increase the perception of these early renders with little overhead.

Path tracing is a well-known light transport algorithm used to render photo-realistic images. However, it is an expensive algorithm with an active area of research for improving its efficiency. In our work, we present a method to measure and visualize the regions of high computational cost for unidirectional path tracers. We have defined metrics to estimate the cost per pixel that can be visualized as a two-dimensional(2D) image. To show its usefulness, we present how our method can visualize the effect of changing material properties, object placement and other scene variables on computational cost. With this insight, the user can make clever choices to improve computational time.

Spectral Monte-Carlo methods are powerful physically-based techniques for simulating wavelength-dependent phenomena such as dispersion. However, compared to tristimulus rendering, they involve sampling the spectral domain, which adds substantial overhead, requiring significantly more samples for noise-free, realistic-looking renders. Thereby, we propose a simple approach to efficiently sample emitters. We precompute a simple 2-dimensional data structure using spectral power distributions of scene emitters. We use it to model a probability distribution function to sample an emitter that yields high path throughput at every intersection when using Next Event Estimation. Our method handles various geometries and spectral distributions of scene emitters, improves convergence, and reduces noise with negligible overhead.