logo

SCIENCE CHINA Information Sciences, Volume 63 , Issue 12 : 224101(2020) https://doi.org/10.1007/s11432-018-9689-9

Semantic part segmentation of single-view point cloud

More info
  • ReceivedSep 15, 2018
  • AcceptedNov 30, 2018
  • PublishedSep 28, 2020

Abstract


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61502023, 61532003).


Supplement

Videos and other supplemental documents.


References

[1] Kalogerakis E, Hertzmann A, Singh K. Learning 3D mesh segmentation and labeling. ACM Trans Graph, 2010, 29: 102. Google Scholar

[2] Charles R Q, Su H, Kaichun M, et al. Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017. 77--85. Google Scholar

[3] Qi C R, Li Y, Su H, et al. PointNet+: deep hierarchical feature learning on point sets in a metric space. 2017,. arXiv Google Scholar

[4] Li Y Y, Bu R, Sun M C, et al. PointCNN: convolution on X-transformed points. 2018,. arXiv Google Scholar

[5] Chen D Y, Tian X P, Shen Y T. On Visual Similarity Based 3D Model Retrieval. Comput Graphics Forum, 2003, 22: 223-232 CrossRef Google Scholar

[6] Chang A X, Funkhouser T, Guibas L J, et al. Shapenet: an information-rich 3D model repository. 2015,. arXiv Google Scholar

[7] van Kaick O, Tagliasacchi A, Sidi O. Prior Knowledge for Part Correspondence. Comput Graphics Forum, 2011, 30: 553-562 CrossRef Google Scholar

[8] Kalogerakis E, Averkiou M, Maji S, et al. 3D shape segmentation with projective convolutional networks. 2017,. arXiv Google Scholar

[9] Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm. In: Proceedings of International Conference on 3-d Digital Imaging and Modeling, 2001. 145--152. Google Scholar