Image-driven simplification

We introduce the notion of image-driven simplification, a framework that us es images to decide which portions of a model to simplify. This is a depart ure from approaches that make polygonal simplification decisions based on g eometry. As with many methods, we use the edge collapse operator to make in cremental changes to a model. Unique to our approach, however, is the use o f comparisons between images of the original model against those of a simpl ified model to determine the cost of an edge collapse. We use common graphi cs rendering hardware to accelerate the creation of the required images. As expected, this method produces models that are close to the original model according to image differences. Perhaps more surprising, however, is that the method yields models that have high geometric fidelity as well. Our app roach also solves the quandary of how to weight the geometric distance vers us appearance properties such as normals, color, and texture. All of these trade-offs are balanced by the image metric. Benefits of this approach incl ude high fidelity silhouettes, extreme simplification of hidden portions of a model, attention to shading interpolation effects, and simplification th at is sensitive to the content of a texture. In order to better preserve th e appearance of textured models, we introduce a novel technique for assigni ng texture coordinates to the new vertices of the mesh. This method is base d on a geometric heuristic that can be integrated with any edge collapse al gorithm to produce high quality textured surfaces.