Image-Error-Based Level of Detail for Landscape Visualization

Abstract

We present a quasi-continuous level of detail method that is based on an image error metric to minimize the visual error. The method is designed for objects of high geometric complexity such as trees. By successive simplifications, it constructs a level of detail hierarchy of unconnected primitives (ellipsoids, lines) to approximate the input models at increasingly coarser levels. The hierarchy is constructed automatically without manual intervention. When rendering roughly 100k model instances at a low visual error compared to rendering the full resolution model, our method is two times faster than billboard clouds.