SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Fast and memory efficient polygonal simplification
Proceedings of the conference on Visualization '98
Applying MDL to learn best model granularity
Artificial Intelligence
An edgebreaker-based efficient compression scheme for regular meshes
Computational Geometry: Theory and Applications
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Mesh Simplification Using Four-Face Clusters
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Measuring non-gaussianity by phi-transformed and fuzzy histograms
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Voronoi-Based extraction of a feature skeleton from noisy triangulated surfaces
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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In this paper a new approach to find an optimal surface representation is described. It is shown that the minimum description length (MDL) principle can be used to select a trade-off between goodness-offit and complexity of decimated mesh representations. A given mesh is iteratively simplified by using different decimation algorithms. At each step the two-part minimum description length is evaluated. The first part encodes all model parameters while the second part encodes the error residuals given the model. A Bayesian approach is used to deduce the MDL term. The shortest code length identifies the optimal trade-off. The method has been successfully tested by various examples.