![]() ![]() We illustrate both abstractions by adapting two different simplification methods to perform their computation using a prototype of our mesh processing sequence API. The two abstractions that are naturally supported by this representation are boundary-based and buffer-based processing. This provides seamless and highly efficient out-of-core access to very large meshes for algorithms that can adapt their computations to this fixed ordering. Mesh access is restricted to a fixed traversal order, but full connectivity and geometry information is available for the active elements of the traversal. At any time, only a small portion of the mesh is kept in-core, with the bulk of the mesh data residing on disk. This representation allows streaming very large meshes through main memory while maintaining information about the visitation status of edges and vertices. A processing sequence represents a mesh as a particular interleaved ordering of indexed triangles and vertices. ![]() We believe that this processing concept will also prove useful for other tasks, such as parameterization, remeshing, or smoothing, for which currently only in-core solutions exist. In this paper we show how out-of-core mesh processing techniques can be adapted to perform their computations based on the new processing sequence paradigm, using mesh simplification as an example. This method is based on a geometric heuristic that can be integrated with any edge collapse algorithm to produce high quality textured surfaces. 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. 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. All of these trade-offs are balanced by the image metric. Our approach also solves the quandary of how to weight the geometric distance versus appearance properties such as normals, color, and texture. Perhaps more surprising, however, is that the method yields models that have high geometric fidelity as well. As expected, this method produces models that are close to the original model according to image differences. We use common graphics rendering hardware to accelerate the creation of the required images. 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. As with many methods, we use the edge collapse operator to make incremental changes to a model. This is a departure from approaches that make polygonal simplification decisions based on geometry. We introduce the notion of image-driven simplification, a framework that uses images to decide which portions of a model to simplify. ![]()
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