Spectral Monte Carlo renderers are capable of reproducing several advanced phenomena of light, such as chromatic dispersion and fluorescence. As spectral renderers must sample the spectral domain, they are typically hampered by a multitude of sampling issues leading to notably poor convergence rates, which are reinforced when realistic emission or reflectance spectra are involved in otherwise simple scenes. We propose pre-estimated spectral rendering, which is a simple method that iteratively builds estimates of spectral radiance distributions before rendering, and subsequently uses these to efficiently guide importance sampling of the spectral domain. Our method significantly lowers variance and reduces chromatic noise with little overhead in multiple difficult scenarios, which we demonstrate with an implementation in a conventional renderer.

In this report, we present our work on improving the administration software and automating parts of the customer service work for Plugify (https://www.plugify.nl), a young Dutch startup that provides an online music act booking platform. We first analyze Plugify's needs, and then discuss how we design, develop, and iterate the systems they require. We leverage and customize pre-existing administration software named ForestAdmin to provide us with a stable administration panel to create and modify data. Furthermore, we rework Plugify's existing internal notification system to be smarter. Then, we create a small number of custom web pages for Plugify's customer service to more easily process relevant data. Finally, we provide Plugify with a system for metrics in an intelligent way. We also discuss how we perform unit, integration and usability testing, to ensure, among others, the stability, reliability, usability, performance and completeness of the systems we have built.