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Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

Yang, Xiaowen; Han, Xie; Li, Qingde; He, Ligang; Pang, Min; Jia, Caiqin

Authors

Xiaowen Yang

Xie Han

Ligang He

Min Pang

Caiqin Jia



Abstract

With the rapid development of point cloud processing technologies and the availability of a wide range of 3D capturing devices, a geometric object from the real world can be directly represented digitally as a dense and fine point cloud. Decomposing a 3D shape represented in point cloud into meaningful parts has very important practical implications in the fields of computer graphics, virtual reality and mixed reality. In this paper, a semantic-driven automated hybrid segmentation method is proposed for 3D point cloud shapes. Our method consists of three stages: semantic clustering, variational merging, and region remerging. In the first stage, a new feature of point cloud, called Local Concave-Convex Histogram, is introduced to first extract saddle regions complying with the semantic boundary feature. All other types of regions are then aggregated according to this extracted feature. This stage often leads to multiple over-segmentation convex regions, which are then remerged by a variational method established based on the narrow-band theory. Finally, in order to recombine the regions with the approximate shapes, order relation is introduced to improve the weighting forms in calculating the conventional Shape Diameter Function. We have conducted extensive experiments with the Princeton Dataset. The results show that the proposed algorithm outperforms the state-of-the-art algorithms in this area. We have also applied the proposed algorithm to process the point cloud data acquired directly from the real 3D objects. It achieves excellent results too. These results demonstrate that the method proposed in this paper is effective and universal.

Journal Article Type Article
Publication Date 2020
Journal IEEE Access
Print ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 8
Pages 40861-40880
APA6 Citation Yang, X., Han, X., Li, Q., He, L., Pang, M., & Jia, C. (2020). Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes. IEEE access : practical innovations, open solutions, 8, 40861-40880. https://doi.org/10.1109/access.2020.2976847
DOI https://doi.org/10.1109/access.2020.2976847
Keywords Three-dimensional displays; Shape; Clustering algorithms; Image segmentation; Feature extraction; Semantics; Surface reconstruction;Semantic-driven; local concave-convex histogram; variational method; shape diameter function
Publisher URL https://ieeexplore.ieee.org/document/9016225

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