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论文类型：期刊论文

发表时间：2018-08-01

发表刊物：COMPUTER-AIDED DESIGN

收录刊物：SCIE

卷号：101

页面范围：72-81

ISSN号：0010-4485

关键字：Semantic; Boundary guidance; Mesh labeling; CNNs

摘要：We propose a novel method for 3D mesh labeling based on a deep learning approach. We train two deep networks to produce initial labels and semantic boundary maps for test meshes. By using dropout technique, discriminative features can be extracted from our deep networks to improve mesh labeling and boundary detection. Given the detected boundary map, a smoother distance field with closed boundary depiction is calculated for succeeding optimization. Then, based on the initial labels, we obtain the final smooth results through a graph-cut optimization guided by the semantic boundary distance field. With the semantic boundary guidance, labeling is improved distinctly, especially, when large mislabeling regions appear or the boundary of initial labels is not reliable. Furthermore, our algorithm is robust to mesh noise, and can handle mixed dataset with meshes from different categories effectively. Experimental results show that our method outperforms the state-of-the-art methods on public benchmarks. (C) 2018 Elsevier Ltd. All rights reserved.