Detection of feature lines in a point cloud by combination of rst order segmentation and graph theory

We present a method to nd closed feature lines which indicate sharp edges in a point cloud in the context of reverse engineering. We start with a rst order segmentation which results in dieren t point clusters. A weighted graph structure is built where vertices correspond to the resulting point clusters and edges connect neigh- boring clusters. By choosing the weights carefully, the minimum spanning tree gives, after removal of some particular edges, a rst approximation of the feature lines. This approximation has many short branches, which we remove with a pruning algorithm. Since the resulting graph consists of many unconnected pieces of feature lines, we introduce an algorithm that grows a part of a feature line and connects it to another part of the same feature line. A nal clean up results in a good polygonal approximation of the feature lines.