Out-of-core simplification of large polygonal models

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Out-of-core simplification of large polygonal models
We present an algorithm for out-of-core simplification of large polygonal datasets that are too complex to fit in main memory. The algorithm extends the vertex clustering scheme of Rossignac and Borrel [13] by using error quadric information for the placement of each cluster's representative vertex, which better preserves fine details and results in a low mean geometric error. The use of quadrics instead of the vertex grading approach in [13] has the additional benefits of requiring less disk space and only a single pass over the model rather than two. The resulting linear time algorithm allows simplification of datasets of arbitrary complexity. In order to handle degenerate quadrics associated with (near) flat regions and regions with zero Gaussian curvature, we present a robust method for solving the corresponding underconstrained leastsquares problem. The algorithm is able to detect these degeneracies and handle them gracefully. Key features of the simplification method includ...
Peter Lindstrom
Added 25 Aug 2010
Updated 25 Aug 2010
Type Conference
Year 2000
Authors Peter Lindstrom
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