Feature-aligned segmentation using correlation clustering

Abstract

We present an algorithm for segmenting a mesh into patches whose boundaries are aligned with prominent ridge and valley lines of the shape. Our key insight is that this problem can be formulated as correlation clustering (CC), a graph partitioning problem originating from the data mining community. The formulation lends two unique advantages to our method over existing segmentation methods. First, since CC is non-parametric, our method has few parameters to tune. Second, as CC is governed by edge weights in the graph, our method offers users direct and local control over the segmentation result. Our technical contributions include the construction of the weighted graph on which CC is defined, a strategy for rapidly computing CC on this graph, and an interactive tool for editing the segmentation. Our experiments show that our method produces qualitatively better segmentations than existing methods on a wide range of inputs.

Simple primitive recognition via hierarchical face clustering

“…Instead of using minima rules, feature lines can also be used for segmentation. Such algorithms first extract ridges and valleys [37] from input meshes, and then remove small features by filtering and extend the major ones to form closed feature loops [20,38]. The enclosed regions are extracted as the final segmentation.…”

“…When processing organic models, our algorithm Fig. 6 Comparisons, left to right, to HMS [19], VMS [11], FAS [20], ours, and a color map. Top to bottom: tessellated CAD models Chess and Sword, scanned mechanical model Cup, and organic shape Bone.…”

“…Our algorithm is slightly slower than Attene's HMS [19] due to the additional computation, but our results are improved significantly. Compared to optimizationbased approaches [11,20], our algorithm is much faster. However, our implementation is not fully optimized, and we hope to further improve the performance in future work.…”

“…We have conducted extensive comparisons with existing representative approaches [11,[18][19][20], and [19], (b) Variational Mesh Segmentation (VMS) [11], (c) Feature-Aligned Segmentation (FAS) [20], and (d) our result. Colors are randomly assigned to segmented patches.…”

“…The results were produced on a machine with an Intel Core i7-7700 CPU with 16 GB RAM and Windows 10 operating system. In the following, we perform a detailed analysis of our method, as well as comparing it to several representative approaches, hierarchical face clustering (HFC) [18], hierarchical mesh segmentation (HMS) [19], variational mesh segmentation (VMS) [11], and feature-aligned segmentation (FAS) [20].…”