Mesh Segmentation with Concavity-aware Fields

Oscar Kin-Chung Au1  Youyi Zheng2   Menglin Chen2  Pengfei Xu2 Chiew-Lan Tai2

IEEE Transaction on Visualization and Computer Graphics

1City University of Hong Kong

2Hong Kong Unversity of Science and Technology


(Top row) Curvature-aware segmentation fields. The fields are defined by setting boundary conditions at the highlighted extreme points (0 at red point and 1 at green point). Observe that the visualized fields have clear boundaries which coincide with the concave seams. Furthermore, one segmentation field can identify multiple concave seams. (Bottom row) The isolines (50) sampled from the field. Uniform sampling leads to dense isolines at the concave seams.

This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme. Isolines sampled from the concavity-aware fields naturally gather at concave seams, serving as good cutting boundary candidates. In addition, the fields provide sufficient information allowing efficient evaluation of the candidate cuts. We perform a summarization of all field gradient magnitudes to define a score for each isoline and employ a score-based greedy algorithm to select the best cuts. Extensive experiments and quantitative analysis have shown that the quality of our segmentations are better than or comparable with existing state-of-the-art more complex approaches.
Concavity-aware Field, Mesh Segmentation, Isolines

author = {Oscar Kin-Chung Au and Youyi Zheng and Menglin Chen and Pengfei Xu and and Chiew-Lan Tai},
title = {Mesh Segmentation with Concavity-aware Fields},
booktitle = {IEEE Trans. Vis. Comp. Graphics},
year = {2011},
volume = {},
number = {},
pages = {To appear}

We would like to thank the anonymous reviewers for their constructive comments and Shauna Dalton for proofreading the final version of the paper. This work was supported in part by grants from the City University of Hong Kong (Project No. 7200148) and the Hong Kong Research Grant Council (Project No. GRF619611).