Surface simplification guided by morph-targets

Many effective automatic surface simplification algorithms have been developed. These automatic algorithms create very plausible results in many cases, but at very low levels of detail they do not preserve the visual appearance of the original model very well. This could be improved if surface simplification algorithms were able to make use of semantic or high-level meaning of models. The idea of our new method using a morph-target-based surface simplification is to use distance information inside the morph-targets to acquire the relative importance of different surface regions without user guidance. Using this additional input the model is simplified by using modified quadric error metrics