Kuan-Loong Yong, Seth Green, Patti McLain CS348B Image Synthesis Final Project Writeup [edited] Fractal Mountain ---------------- The fractal mountain is modeled as a polyset created from a heightfield. A diffuse and specular texture map are algorithmically generated from the heightfield data to provide the same effect as a volume-based texture. The fractal terrain was generated using fractal mountain algorithms from The Science of Fractal Images [5]. Surface normals are determined by using a finite difference method. Sea water is generated by scaling certain parts of the terrain by a small fraction and shading them the color of the sea. This has the disadvantage of forcing the water to have the same fractal dimension as the mountains. The sea color was determined by duplicating colors from a scanned photograph. The color scheme of the mountain was based of height and added FBM noise to simulate rock strata. Snow is algorithmically grown on the mountain using a probability density function which takes into account the height of the point and itıs slope. High or flat areas are likely to have snow, while low or steep mountain faces are not. Rivers are also algorithmically grown. Rivers begin at randomly chosen nucleation points and follow the path of steepest decent to the sea. Often a river will encounter a local minima in its path. To avoid infinite loops a history of the river path is kept to avoid backtracking. If the river completely surrounds itself on all sides, a small jump proportional to the width of the river is taken in a random direction. In this way the river will pool in valleys until it climbs the walls. A corresponding specular map is also generated to add reflective properties to the snow and water but not to the mountain itself. [5] Peitgen and Saupe 1988 The Science of Fractal Images Springer-Verlag, pp 100-101