• Laser & Optoelectronics Progress
  • Vol. 57, Issue 12, 121104 (2020)
Renzhong Li* and Zhewen Liu
Author Affiliations
  • School of Electronic and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • show less
    DOI: 10.3788/LOP57.121104 Cite this Article Set citation alerts
    Renzhong Li, Zhewen Liu. New Segmentation Method Combining Three-Dimensional Point Cloud Skeleton Points and Feature Points[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121104 Copy Citation Text show less
    References

    [1] Li R Z, Yang M, Liu Y Y et al. A uniform simplification algorithm for scattered point cloud[J]. Acta Optica Sinica, 37, 0710002(2017).

    [2] Yao C J. Research on registration of LIDAR point data and remote sensing images[D]. Wuhan: Wuhan University, 1-4(2010).

    [3] Yu C H. Research on acquisition and visualization of three dimensional data for collection of cultural relics[D]. Guangzhou: South China University of Technology, 1-5(2016).

    [4] Lü Y F. Research on driving control layout and pedal device reverse engineering of FSAE project[D]. Yangzhou: Yangzhou University, 24-29(2018).

    [5] Zhu D H, Guo H, Su W[M]. Point cloud library, 338-342(2012).

    [6] Zhang Q, Li C K, Li J X et al. Planar point cloud segmentation based on the weighted average of adjusted normal vector[J]. Geography and Geo-Information Science, 31, 45-48(2015).

    [7] Jagannathan A, Miller E L. Three-dimensional surface mesh segmentation using curvedness-based region growing approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 2195-2204(2007).

    [8] Zhang X Y, Tian Q G, Ge B Z. Multi-constrained segmentation of 3D human point-cloud[J]. Journal of Computer Applications, 35, 830-834(2015).

    [9] Yi L, Zhao W, Wang H et al. GSPN: generative shape proposal network for 3D instance segmentation in point cloud. [C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. IEEE, 3947-3956(2019).

    [10] Lu R R, Zhu F, Wu Q X et al. A fast segmenting method for scenes with stacked plate-shaped objects[J]. Acta Optica Sinica, 39, 0412003(2019).

    [11] Serino L, Arcelli C. S di Baja G S. From skeleton branches to object parts[J]. Computer Vision and Image Understanding, 129, 42-51(2014).

    [12] Huang H, Wu S H, Cohen-Or D et al. L1-medial skeleton of point cloud[J]. ACM Transactions on Graphics, 32, 65-73(2013).

    [13] Wang X H, Wu L S, Chen H W et al. Feature line extraction from a point cloud based on region clustering segmentation[J]. Acta Optica Sinica, 38, 1110001(2018).

    [14] Lowe D G. Object recognition from local scale-invariant features. [C]∥ Proceedings of the Seventh IEEE International Conference on Computer Vision, September 20-25, 1999, Kerkyra, Greece. New York: IEEE, 1150-1157(1999).

    [15] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1023/B:VISI.0000029664.99615.94&rfr_id=trans/tp/2008/10/ttp2008101683.htm

    [16] Flint A. Dick A, van den Hengel A. Thrift: local 3D structure recognition. [C]∥9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), December 3-5, 2007, Glenelg, Australia. IEEE, 182-188(2007).

    [17] Scovanner P, Ali S, Shah M. A 3-dimensional sift descriptor and its application to action recognition[C]∥Proceedings of the 15th international conference on Multimedia-MULTIMEDIA '07, September 24-29, 2007, Augsburg, Germany. Ne(2007).

    Renzhong Li, Zhewen Liu. New Segmentation Method Combining Three-Dimensional Point Cloud Skeleton Points and Feature Points[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121104
    Download Citation