Please note: This PhD defence will take place in DC 3317.
Petri Varsa, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Gladimir V.G. Baranoski
Interactions between light and matter give rise to an abundance of natural phenomena. Common examples include those between light and atmospheric ice crystals producing halos, and between light and liquid water droplets producing rainbows. However, interesting effects may also be observed when light impinges upon more dense materials such as snow. These may be noted as changes to the appearance of the material resulting from variations in the characteristics of the material itself. In some cases, these appearance changes may even manifest themselves as dramatic changes in colour. In this thesis, we study snow as a material and reproduce such phenomena by simulating light interactions with virtual snow samples.
Accordingly, this work presents a first-principles framework for simulating light transport through snow. Data and information that describe the characteristics of snowpacks are obtained from the literature and used to devise a digital representation of them suitable for predicatively modelling light interactions with snow. The employed formulation allows for different virtual snow samples to be investigated. Simulated rays of light are cast into a virtual snow sample, and these rays are reflected and refracted until they exit from the surface of the sample, are transmitted through the sample or are absorbed. The modelling results are recorded as spectral response datasets for evaluation and analysis. These datasets are then compared with measured data and observations reported in the literature in order to assess the simulations’ fidelity.
There are a number of study areas where such a framework can make a contribution. In this thesis, we discuss such contributions to two fields of research, namely, computer graphics and remote sensing. From a computer graphics perspective, the outputs of simulating light interactions with snow may be used in natural phenomena visualizations employed for educational and entertainment purposes. From a remote sensing perspective, the simulations may be used to conduct in silico experiments that help to shed light on topics that are actively being studied. Furthermore, the simulation outputs may also be used as data products in themselves, to make comparisons against remotely acquired data and support other modelling initiatives.
The proposed framework presented in this thesis encapsulates a body of work that is expected to advance the state of the art of snow appearance modelling using a multi-faceted approach. The foundation of the framework is a novel radiative transfer model of light impingement on snow, whose predictive capabilities are extensively evaluated. Then, data products produced by this framework are used to address open questions in the two fields of interest, i.e., computer graphics and remote sensing. In particular, we describe a method to include the complex, visual phenomena that are predicted by the radiative transfer model introduced here into a traditional rendering pipeline. We also make use of the proposed framework to investigate scientific investigations (e.g., the absorption of solar radiation by snow and the effect that this has on avalanche prediction) with potential interdisciplinary applications.