• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0828004 (2022)
Wen Hao1、2、*, Hongxiao Wang1、2, and Yang Wang1、2
Author Affiliations
  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an , Shaanxi 710048, China
  • 2Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an , Shaanxi 710048, China
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    DOI: 10.3788/LOP202259.0828004 Cite this Article Set citation alerts
    Wen Hao, Hongxiao Wang, Yang Wang. Semantic Segmentation of Three-Dimensional Point Cloud Based on Spatial Attention and Shape Feature[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0828004 Copy Citation Text show less
    References

    [1] Fang W, Ding Y W, Zhang F H et al. DOG: a new background removal for object recognition from images[J]. Neurocomputing, 361, 85-91(2019).

    [2] Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques[J]. Neurocomputing, 338, 321-348(2019).

    [3] Zhang J Y, Zhao X L, Chen Z. Review of semantic segmentation of point cloud based on deep learning[J]. Laser & Optoelectronics Progress, 57, 040002(2020).

    [4] Wen P, Cheng Y L, Yu W C. Point cloud classification methods based on deep learning: a review[J]. Laser & Optoelectronics Progress, 58, 1600003(2021).

    [5] Guo Y L, Wang H Y, Hu Q Y et al. Deep learning for 3D point clouds: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 4338-4364(2021).

    [6] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

    [7] Qi C R, Yi L, Su H et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space[C], 30, 5099-5108(2017).

    [8] Jiang M Y, Wu Y R, Zhao T Q. PointSIFT: a SIFT-like network module for 3D point cloud semantic segmentation[EB/OL]. https://arxiv.org/abs/1807.00652

    [9] Shang P F, Chen Y, Lü W J et al. A point cloud semantic segmentation network considering normals[J/OL]. Laser & Optoelectronics Progress, 1-16. http://kns.cnki.net/kcms/detail/31.1690.tn.20210802.1730.058.html

    [10] Li Y Y, Bu R, Sun M C et al. PointCNN: convolution on X-transformed points[C], 828-838(2018).

    [11] Hua B S, Tran M K, Yeung S K. Pointwise convolutional neural networks[C], 984-993(2018).

    [12] Wu W X, Qi Z, Fuxin L. PointConv: deep convolutional networks on 3D point clouds[C], 9613-9622(2019).

    [13] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 1-12(2019).

    [14] Lan S Y, Yu R C, Yu G et al. Modeling local geometric structure of 3D point clouds using geo-CNN[C], 998-1008(2019).

    [15] Zhang K G, Hao M, Wang J et al. Linked dynamic graph CNN: learning on point cloud via linking hierarchical features[EB/OL]. https://arxiv.org/abs/1904.10014

    [16] Weinmann M, Jutzi B, Hinz S et al. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286-304(2015).

    [17] Liu Y C, Fan B, Xiang S M et al. Relation-shape convolutional neural network for point cloud analysis[C], 8887-8896(2019).

    [18] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C], 3141-3149(2019).

    [19] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C], 1912-1920(2015).

    [20] Yi L, Kim V G, Ceylan D et al. A scalable active framework for region annotation in 3D shape collections[J]. ACM Transactions on Graphics, 35, 1-12(2016).

    [21] Armeni I, Sener O, Zamir A R et al. 3D semantic parsing of large-scale indoor spaces[C], 1534-1543(2016).

    [22] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition[C], 922-928(2015).

    [23] Simonovsky M, Komodakis N. Dynamic edge-conditioned filters in convolutional neural networks on graphs[C], 29-38(2017).

    [24] Li J X, Chen B M, Lee G H. SO-net: self-organizing network for point cloud analysis[C], 9397-9406(2018).

    [25] Te G S, Hu W, Zheng A M et al. RGCNN: regularized graph CNN for point cloud segmentation[C], 746-754(2018).

    [26] Klokov R, Lempitsky V. Escape from cells: deep Kd-networks for the recognition of 3D point cloud models[C], 863-872(2017).

    [27] Chen C, Fragonara L Z, Tsourdos A. GAPNet: graph attention based point neural network for exploiting local feature of point cloud[EB/OL]. https://arxiv.org/abs/1905.08705

    [28] Xie S N, Liu S N, Chen Z Y et al. Attentional ShapeContextNet for point cloud recognition[C], 4606-4615(2018).

    [29] Huang Q G, Wang W Y, Neumann U. Recurrent slice networks for 3D segmentation of point clouds[C], 2626-2635(2018).

    [30] Roynard X, Deschaud J E, Goulette F. Classification of point cloud scenes with multiscale voxel deep network[EB/OL]. https://arxiv.org/abs/1804.03583

    [31] Tchapmi L, Choy C, Armeni I et al. SEGCloud: semantic segmentation of 3D point clouds[C], 537-547(2017).

    [32] Landrieu L, Simonovsky M. Large-scale point cloud semantic segmentation with superpoint graphs[C], 4558-4567(2018).

    [33] Xu X, Shuai H, Liu Q S. Octant convolutional neural network for 3D point cloud analysis[J/OL]. Acta Automatica Sinica, 1-10. http://kns.cnki.net/kcms/detail/11.2109.TP.20200811.1020.002.html

    [34] Yang J, Dang J S. Semantic segmentation of 3D point cloud based on contextual attention CNN[J]. Journal on Communications, 41, 195-203(2020).

    Wen Hao, Hongxiao Wang, Yang Wang. Semantic Segmentation of Three-Dimensional Point Cloud Based on Spatial Attention and Shape Feature[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0828004
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