Spectral rendering has received increasing attention in recent years. Yet, solutions to define spectral reflectances are mostly limited to uplifting techniques which deterministically augment existing RGB inputs. Only recently has uplifting been able to ensure a certain surface appearance under direct illuminants. Yet, prior work in this area limits artist expressiveness and is not well suited for designing the appearance of a scene, as indirect illumination is ignored entirely.

We present an uplifting technique with fine-grained spectral appearance control under direct and indirect illumination, even enabling the placement of spectral constraints in a specific scene. Our approach allows for a flexible authoring process, and solves for the resulting spectra efficiently. Additionally, we show that our method’s memory overhead during rendering is kept small, by introducing a compact spectral texture format.@en

Spectral rendering is a crucial solution for photorealistic rendering. However, most available texture assets are RGB-only, and access to spectral content is limited. Uplifting methods that recover full spectral representations from RGB inputs have therefore received much attention. Yet, most methods are deterministic, while, in reality, there is no one-to-one mapping. As a consequence, the appearance of uplifted textures is fully determined under all illuminants. Hereby, metamers, which are materials with differing spectral responses that appear identical under a specific illumination, are excluded. We propose a method which makes this uplifting process controllable. Hereby, a user can define texture appearance under various lighting conditions, leading to a greatly increased flexibility for content design. Our method determines the space of possible metameric manipulations and enables interactive adjustments, while maintaining a set of user-specified appearance constraints. To achieve this goal, we formulate the problem as a constrained optimization, building upon an interpolation scheme and data-based reflectance generation, which maintain plausibility. Besides its value for artistic control, our solution is lightweight and can be executed on the fly, which keeps its memory consumption low and makes it easy to integrate into existing frameworks.

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Spectral Monte Carlo rendering can simulate advanced light phenomena, such as chromatic dispersion, but typically shows a slow convergence behavior. Properly sampling the spectral domain can be challenging in scenes with many complex spectral distributions. To this end, we propose a multi-pass approach. We build and store coarse screen-space estimates of incident spectral radiance and use these to then importance sample the spectral domain. Hereby, we lower variance and reduce noise with little overhead. Our method handles challenging scenarios with difficult spectral distributions, many different emitters, and participating media. Finally, it can be integrated into existing spectral rendering methods for an additional acceleration.@en

t-distributed Stochastic Neighbour Embedding (t-SNE) has become a standard for exploratory data analysis, as it is capable of revealing clusters even in complex data while requiring minimal user input. While its run-time complexity limited it to small datasets in the past, recent efforts improved upon the expensive similarity computations and the previously quadratic minimization. Nevertheless, t-SNE still has high runtime and memory costs when operating on millions of points. We present a novel method for executing the t-SNE minimization. While our method overall retains a linear runtime complexity, we obtain a significant performance increase in the most expensive part of the minimization. We achieve a significant improvement without a noticeable decrease in accuracy even when targeting a 3D embedding. Our method constructs a pair of spatial hierarchies over the embedding, which are simultaneously traversed to approximate many N-body interactions at once. We demonstrate an efficient GPGPU implementation and evaluate its performance against state-of-the-art methods on a variety of datasets.@en