Viewpoint Entropy-Driven Simplification

Abstract

In this paper, a new viewpoint-based simplification approach is proposed for polygonal meshes. This approach is driven by an information-theoretic metric, viewpoint entropy, which measures the amount of information from a scene or object that arrives at a certain viewpoint. Our algorithm applies the best half-edge collapse as a decimation criterion and uses the variation of viewpoint entropy to measure the edge collapse error. Compared to pure geometric-based simplifications, the models produced by our method are closer to the original model according to visual similarity. Our approach also achieves a higher simplification in hidden interiors, by being able to remove them all and to leave the visible areas of the mesh intact. Models generated by CAD applications can benefit from this feature, since these models are usually constructed by assembling smaller objects which can become partially hidden during joining operations. The main application of our method is for video games where models come from CAD applications and are geometrically not very complex, a few thousand polygons at the most, and in which visual similarity is the most important requirement.