• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2028004 (2021)
Ming Lai, Jiankang Zhao*, Chuanqi Liu, Chao Cui, and Haihui Long
Author Affiliations
  • Department of Instrument Science & Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • show less
    DOI: 10.3788/LOP202158.2028004 Cite this Article Set citation alerts
    Ming Lai, Jiankang Zhao, Chuanqi Liu, Chao Cui, Haihui Long. Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2028004 Copy Citation Text show less
    References

    [1] Wang R S, Peethambaran J, Chen D. LiDAR point clouds to 3-D urban models: a review[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 606-627(2018).

    [2] Liu X Y. Airborne LiDAR for DEM generation: some critical issues[J]. Progress in Physical Geography: Earth and Environment, 32, 31-49(2008).

    [3] Liu T, Su W, Wang C et al. A method of estimating maize LAI using airborne LiDAR data[J]. Journal of China Agricultural University, 21, 104-111(2016).

    [4] Weinmann M, Jutzi B, Mallet C. Feature relevance assessment for the semantic interpretation of 3D point cloud data[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-5/W2, 313-318(2013).

    [5] Zhang J X, Lin X G, Ning X G. SVM-based classification of segmented airborne LiDAR point clouds in urban areas[J]. Remote Sensing, 5, 3749-3775(2013).

    [6] Shi X S, Cheng Y L, Zhao Z Y et al. Point cloud classification algorithm based on IPTD and SVM[J]. Laser & Optoelectronics Progress, 56, 161002(2019).

    [7] Xue D D, Cheng Y L, Shi X S et al. Point clouds classification algorithm based on cloth filtering algorithm and improved random forest[J]. Laser & Optoelectronics Progress, 57, 221017(2020).

    [8] 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, 5434, 1-27(2020).

    [9] Huang J, You S Y. Point cloud labeling using 3D convolutional neural network[C]. //2016 23rd International Conference on Pattern Recognition (ICPR), December 4-8, 2016, Cancun, Mexico., 2670-2675(2016).

    [10] Lawin F J, Danelljan M, Tosteberg P et al. Deep projective 3D semantic segmentation[M]. //Felsberg M, Heyden A, Krüger N. Computer analysis of images and patterns. Lecture notes in computer science, 10424, 95-107(2017).

    [11] Qi C R, Hao S, 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, 77-85(2017).

    [12] Li Y Y, Bu R, Sun M C et al. PointCNN: convolution on X-transformed points[C]. //Advances in Neural Information Processing Systems, December 3-8, 2018, Montréal, Canada, 820-830(2018).

    [13] Thomas H, Qi C R, Deschaud J E et al. KPConv: flexible and deformable convolution for point clouds[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South), 6410-6419(2019).

    [14] Boulch A. ConvPoint: continuous convolutions for point cloud processing[J]. Computers & Graphics, 88, 24-34(2020).

    [15] Hou X D, Yu X X, Liu H P. 3D point cloud classification and segmentation model based on graph convolutional network[J]. Laser & Optoelectronics Progress, 57, 181019(2020).

    [16] Phan A V, Nguyen M L, Nguyen Y L H et al. DGCNN: a convolutional neural network over large-scale labeled graphs[J]. Neural Networks, 108, 533-543(2018).

    [17] Huang Q G, Wang W Y, Neumann U. Recurrent slice networks for 3D segmentation of point clouds[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2626-2635(2018).

    [18] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]. //Advances in Neural Information Processing Systems, December 3-8, 2017, Long Beach, CA, USA. Canda, 5099-5108(2017).

    [19] Jiang M Y, Wu Y R, Zhao T Q et al. PointSIFT: a SIFT-like network module for 3D point cloud semantic segmentation[EB/OL]. (2018-07-02)[2020-11-10]. https://arxiv.org/abs/1807.00652

    [20] Li W Z, Wang F D, Xia G S. A geometry-attentional network for ALS point cloud classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 26-40(2020).

    [21] Oktay O, Schlemper J, Folgoc L L et al. Attention U-Net: learning where to look for the pancreas[EB/OL]. (2018-08-11)[2020-11-10]. https://arxiv.org/abs/1804.03999

    [22] Dai Y M, Gieseke F, Oehmcke S et al. Attentional feature fusion[EB/OL]. (2020-09-29)[2020-11-10]. https://arxiv.org/abs/2009.14082

    [23] Niemeyer J, Rottensteiner F, Soergel U. Contextual classification of lidar data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152-165(2014).

    [24] Yousefhussien M, Kelbe D J, Ientilucci E J et al. A multi-scale fully convolutional network for semantic labeling of 3D point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 191-204(2018).

    [25] Yang Z S, Tan B, Pei H K et al. Segmentation and multi-scale convolutional neural network-based classification of airborne laser scanner data[J]. Sensors, 18, 3347(2018).

    [26] Zhao R B, Pang M Y, Wang J D. Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network[J]. International Journal of Geographical Information Science, 32, 960-979(2018).

    Ming Lai, Jiankang Zhao, Chuanqi Liu, Chao Cui, Haihui Long. Semantic Segmentation of LiDAR Point Cloud Based on CAFF-PointNet[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2028004
    Download Citation