Depth Image Point Cloud Segmentation Using Spatial Projection

Qingda Guo; Yanming Quan

doi:10.3788/AOS202040.1815001

[1] Nguyen A, Le B. 3D point cloud segmentation: a survey. [C]//2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), November 12-15, 2013, Manila, Philippines. New York: IEEE, 225-230(2013).

[2] Ou X L, Kuang X L, Ni W Y. Summarization on 3D scattered point cloud segmentation[J]. Journal of Hunan University of Technology, 24, 45-49(2010).

[3] Besl P J, Jain R C. Segmentation through variable-order surface fitting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 167-192(1988).

[4] Koh J, Suk M, Bhandarkar S M. A multi-layer Kohonen's self-organizing feature map for range image segmentation. [C]//IEEE International Conference on Neural Networks, March 28-April 1,1993, San Francisco, CA, USA, New York: IEEE, 4725511(1993).

[5] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 24, 381-395(1981).

[6] 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).

[7] Fan X H, Xu G L, Li W L et al. Target segmentation method for three-dimensional LiDAR point cloud based on depth image[J]. Chinese Journal of Lasers, 46, 0710002(2019).

[8] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, December 3-6, 2012, South Lake Tahoe, NV, U, 1097-1105(2012).

[9] Klokov R, Lempitsky V. Escape from cells: deep kd-networks for the recognition of 3D point cloud models. [C]//2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 863-872(2017).

[10] Wang P S, Liu Y, Guo Y X et al. O-CNN: octree-based convolutional neural networks for 3D shape analysis[J]. ACM Transactions on Graphics, 36, 72(2017).

[11] Riegler G, Ulusoy A O, Geiger A. OctNet: learning deep 3D representations at high resolutions. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 6620-6629(2017).

[12] Charles R Q, Su H, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 77-85(2017).

[13] Qi C R, Yi L, Su H et al. -06-07)[2020-04-28], org/abs/1706, 02413(2017). https://arxiv.

[14] Wu Y L, Quan Y M, Guo Q D. Research on calibration of structured light system based on local homography matrix[J]. Science Technology and Engineering, 16, 186-189, 195(2016).

[15] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62-66(1979).