Geração procedural de cenários 3D de cânions com foco em jogos digitais

• Autor(a) principal: Carli, Daniel Michelon de
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• Texto Completo: http://repositorio.ufsm.br/handle/1/5394
• topic: Geração procedural; Terreno; Cânion; Cenário 3D; Algoritmo de Dijkstra; Ruído de Perlin; Procedural generation techniques; Terrain generation; Canyons; 3D Scenes; Mean shift; Dijkstra s algorithm; Perlin noise; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

This Master s thesis proposes a non-assisted procedural method for 3D canyons scenes generation based on techniques of computer graphics, computer vision and graph search algorithm. In order to define all the features to be reproduced in our scenes, we have analyzed several images of real canyons and have categorized them in two canyon features models: a recursive and an ordinary one. The proposed approach manipulates a heightmap, created using Perlin noise, in order to imitate the geological features formation previously analyzed. Several parametrizations are used to guide and constraint the generation of terrains, canyons features, course of river, plain areas, soft slope regions, cliffs and plateaus. This work also uses the Mean Shift algorithm as mechanism of segmentation to define regions of interest. A binary mask, with plain areas, is defined based on a threshold operation by a given data set provided by the Mean Shift algorithm. Thereafter a connected-component labeling algorithm is executed using the previously binary mask. This algorithm finds all plains centroids. Right after that, the Dijkstra s algorithm is performed in order to connect all plain areas, creating a valid path between the centroids. The Dijkstra s algorithm is executed again to define the river s course. Finally, a Gaussian smoothing operation is applied to interpolate the soft slope regions. The combination of all those techniques produces as a result automatically generated feature-rich canyons.