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Artistic Rendering of Natural Environments

Mountain panorama maps are aerial view paintings that depict complex, three-dimensional landscapes in a pleasing and understandable way. Painters and cartographers have developed techniques to create such artistic landscapes for centuries, but the process remains difficult and time-consuming. 
In this dissertation, we derive principles and heuristics for panorama map creation of mountainous terrain from a perceptual and artistic analysis of a panorama map of Yellowstone National Park painted by two different artists, Heinrich Berann and James Niehues. We then present methods to automatically produce landscape renderings in the visual style of the panorama map. Our algorithms rely on USGS terrain and its corresponding classification data. Our surface textures are generated using perceptual metrics and artistic considerations, and use the structural information present in the terrain to guide the automatic placement of image space strokes for natural surfaces such as forests, cliffs, snow, and water. 
An integral part of automatic rendering is choosing a viewpoint that is meaningful and representative of the landscape. In this dissertation we examine the automatic generation of well-composed and purposeful images in the context of mountainous terrain. We explore a set of criteria based on utility, perception, and aesthetics applicable to natural outdoor scenes. We also propose a method that uses the criteria to produce renderings of terrain scenes automatically. 
Finally, arising from difficulties we encountered using color transfer to improve the final rendered colors of our panorama map renderings, we propose a novel color model, oRGB, that is based on opponent color theory. Like HSV, it is designed specifically for computer graphics. However, it is also designed to work well for computational applications such as color transfer, where HSV falters. Despite being geared towards computation, oRGB's natural axes facilitate HSV-style color selection and manipulation. 
oRGB also allows for new applications such as a quantitative cool-to-warm metric, intuitive color manipulations and variations, and simple gamut mapping. This new color model strikes a balance between simplicity and the computational qualities of color spaces such as CIE L*a*b*.