(Georgia Institute of Technology, 1999)
Lindstrom, Peter; Turk, Greg
We introduce the notion of image-driven simplification, a framework that uses images to decide which portions of a model to simplify. This is a departure from approaches that make polygonal simplification decisions based on geometry. As with many methods, we use the edge collapse operator to make incremental changes to a model. Unique to our approach, however, is the use of comparisons between images of the original model against those of a simplified model to determine the cost of an edge collapse. We use common graphics 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 approach also solves the quandary of how to weight the geometric distance versus appearance properties such as normals, color and texture. All of these tradeoffs are balanced by the image metric. Benefits of this approach include high fidelity silhouettes, extreme simplification of hidden portions of a model, attention to shading interpolation effects, and simplification that is sensitive to the content of a texture. In order to better preserve the appearance of textured models, we introduce a novel technique for assigning texture coordinates to the new vertices of the mesh. This method is based on a geometric heuristic that can be integrated with any edge collapse algorithm to produce high quality textured surfaces.
(Georgia Institute of Technology, 1998)
Lindstrom, Peter; Turk, Greg
Conventional wisdom says that in order to produce high-quality simplified polygonal models, one must retain and use information about the original model during the simplification process. We demonstrate that excellent simplified models can be produced without the need to compare against information from the original geometry while performing local changes to the model. We use edge collapses to perform simplification, as do a number of other methods. We select the position of the new vertex so that the original volume of the model is maintained and we minimize the per-triangle change in volume of the tetrahedra swept out by those triangles that are moved. We also maintain surface area near boundaries and minimize the per-triangle area changes. Calculating the edge collapse priorities and the positions of the new vertices requires only the face connectivity and the the vertex locations in the intermediate model. This approach is memory efficient, allowing the simplification of very large polygonal models, and it is also fast. Moreover, simplified models created using this technique compare favorably to a number of other published simplification methods in terms of mean geometric error.