For graphical applications, these point-clouds are usually transformed into polygonal meshes or spline patches. However, these representations are difficult to manipulate, and require a large amound of data. Computer vision approaches can recover models from sparse range data. However, these methods are usually restricted to specific, fairly simple models. Easy to edit.
Parameters are logical model features.
The input to our algorithm beside the range data is an input model class or hierarchy including an algebraic model description from which the model can be evaluated, constraints on curves in the final model, and code for an initial guess for the root model of the hierarchy. Otherwise, exit to step 5. Refine curves further Smooth model to prevent overfitting Steps 2-4 constitute the greedy recognition phase where an appropriate model is chosen from those encoded by the input hierarchy. These illustrate some of our results. More information will be found in the paper.