结合残差动态图卷积与特征强化的点云分类

doi:10.16088/j.issn.1001-6600.2023021901

Abstract

Abstract: In order to improve the performance and robustness of point cloud classification, a point cloud classification network combining residual dynamic graph convolution and feature enhancement is proposed. Multi-direction coding method is adopted to select the nearest neighbor points in the spatial multi-direction of the local neighborhood center point to enrich the point cloud features. The residual dynamic graph convolution is used to extract features, and the residual structure is used to deeply fuse local features and global features to effectively alleviate the network degradation problem. The reinforcement spatial attention module is constructed so that the network can learn to adaptively assign weights to different neighborhood features in the spatial domain, enhance useful features, and suppress redundant features. High-level links are used to retain more feature information. Experiments show that the overall classification accuracy of the above models on the ModelNet10 and ModelNe40 data sets is 94.81% and 93.62% respectively, with higher classification accuracy and stronger robustness, which is superior to the existing advanced methods.

Key words: point cloud classification, residual learning, dynamic graph convolution, spatial attention, robustness

CLC Number: TP391.41

LIANG Zhengyou, CAI Junmin, SUN Yu, CHEN Lei. Point Cloud Classification Based on Residual Dynamic Graph Convolution and Feature Enhancement[J].Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 37-48.

References

[1] MIRZAEI K, ARASHPOUR M, ASADI E, et al. 3D point cloud data processing with machine learning for construction and infrastructure applications: a comprehensive review[J]. Advanced Engineering Informatics, 2022, 51: 101501. DOI: 10.1016/j.aei.2021.101501.
[2] WANG W G, LAI Q X, FU H Z, et al. Salient object detection in the deep learning era: an in-depth survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6): 3239-3259. DOI: 10.1109/TPAMI.2021.3051099.
[3] 朱勇建, 罗坚, 秦运柏, 等. 基于光度立体和级数展开法的金属表面缺陷检测方法[J]. 广西师范大学学报(自然科学版), 2020, 38(6): 21-31. DOI: 10.16088/j.issn.1001-6600.2020.06.003.
[4] 薛其威, 伍锡如. 基于多模态特征融合的无人驾驶系统车辆检测[J]. 广西师范大学学报(自然科学版), 2022, 40(2): 37-48. DOI: 10.16088/j.issn.1001-6600.2021072002.
[5] SU H, MAJI S, KALOGERAKIS E, et al. Multi-view convolutional neural networks for 3d shape recognition[C]// 2015 IEEE International Conference on Computer Vision (ICCV). Los Alamitos, CA: IEEE Computer Society, 2015: 945-953. DOI: 10.1109/ICCV.2015.114.
[6] MATURANA D, SCHERER S. VoxNet: a 3D convolutional neural network for real-time object recognition[C]// 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Los Alamitos, CA: IEEE Computer Society, 2015: 922-928. DOI: 10.1109/IROS.2015.7353481.
[7] QI C R, 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). Los Alamitos, CA: IEEE Computer Society, 2017: 77-85. DOI: 10.1109/CVPR.2017.16.
[8] 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 30 (NIPS 2017). Red Hook, NY: Curran Associates Inc., 2017: 5105-5114.
[9] ZHAO H S, JIANG L, FU C W, et al. PointWeb: enhancing local neighborhood features for point cloud processing[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2019: 5560-5568. DOI: 10.1109/CVPR.2019.00571.
[10] YAN X, ZHENG C D, LI Z, et al. PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 5588-5597. DOI: 10.1109/CVPR42600.2020.00563.
[11] LI J X, CHEN B M, LEE G H. SO-Net: self-organizing network for point cloud analysis[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 9397-9406. DOI: 10.1109/CVPR.2018.00979.
[12] LI Y Y, BU R, SUN M C, et al. PointCNN: convolution on X-transformed points[C]// Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Red Hook, NY: Curran Associates Inc., 2018: 828-838.
[13] WU W X, QI Z A, LI F X. PointConv: deep convolutional networks on 3D point clouds[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 9613-9622. DOI: 10.1109/CVPR.2019.00985.
[14] LIU Y C, FAN B, XIANG S M, et al. Relation-shape convolutional neural network for point cloud analysis[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 8887-8896. DOI: 10.1109/CVPR.2019.00910.
[15] 顾砾, 季怡, 刘纯平. 基于多模态特征融合的三维点云分类方法[J]. 计算机工程, 2021, 47(2): 279-284. DOI: 10.19678/j.issn.1000-3428.0057147.
[16] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2016: 2818-2826. DOI: 10.1109/CVPR.2016.308.
[17] SIMONOVSKY M, KOMODAKIS N. Dynamic edge-conditioned filters in convolutional neural networks on graphs[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 29-38. DOI: 10.1109/CVPR.2017.11.
[18] WANG Y, SUN Y B, LIU Z W, et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 2019, 38(5): 146. DOI: 10.1145/3326362.
[19] LIN Z H, HUANG S Y, WANG Y C F. Convolution in the cloud: learning deformable kernels in 3D graph convolution networks for point cloud analysis[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 1797-1806. DOI: 10.1109/CVPR42600.2020.00187.
[20] ZHANG K E, HAO M, WANG J, et al. Linked dynamic graph CNN: learning on point cloud via linking hierarchical features[EB/OL]. (2019-04-22)[2023-02-19]. https://arxiv.org/abs/1904.10014. DOI: 10.48550/arXiv.1904.10014.
[21] 梁振明, 翟正利, 周炜. 基于多尺度动态图卷积网络的3D点云分类[J]. 计算机应用与软件, 2021, 38(5): 263-267, 306. DOI: 10.3969/j.issn.1000-386x.2021.05.043.
[22] 魏天琪, 郑雄胜. 基于深度学习的三维点云分类方法研究[J]. 计算机应用研究, 2022, 39(5): 1289-1296. DOI: 10.19734/j.issn.1001-3695.2021.10.0469.
[23] YANG J C, ZHANG Q, NI B B, et al. Modeling point clouds with self-attention and Gumbel subset sampling[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 3318-3327. DOI: 10.1109/CVPR.2019.00344.
[24] 沈露, 杨家志, 周国清, 等. 集自注意力与边卷积的点云分类分割模型[J/OL]. 计算机工程与应用: 1-10[2023-02-19]. http://kns.cnki.net/kcms/detail/11.2127.TP.20221019.1414.006.html.
[25] WU Z R, SONG S R, KHOSLA A, et al. 3D ShapeNets: a deep representation for volumetric shapes[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2015: 1912-1920. DOI: 10.1109/CVPR.2015.7298801.
[26] 王文曦, 李乐林. 深度学习在点云分类中的研究综述[J]. 计算机工程与应用, 2022, 58(1): 26-40. DOI: 10.3778/j.issn.1002-8331.2105-0200.
[27] 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)[2023-02-19]. https://arxiv.org/abs/1807.00652. DOI: 10.48550/arXiv.1807.00652.
[28] VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	DONG Shulong, MA Jiangming, XIN Wenjie. Research Progress and Trend of Landscape Visual Evaluation —Knowledge Atlas Analysis Based on CiteSpace[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 1 -13 .
[2]	GUO Jialiang, JIN Ting. Semantic Enhancement-Based Multimodal Sentiment Analysis[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 14 -25 .
[3]	WU Zhengqing, CAO Hui, LIU Baokai. Chinese Fake Review Detection Based on Attention Convolutional Neural Network[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 26 -36 .
[4]	OUYANG Shuxin, WANG Mingjun, RONG Chuitian, SUN Huabo. Anomaly Detection of Multidimensional QAR Data Based on Improved LSTM[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 49 -60 .
[5]	LI Yiyang, ZENG Caibin, HUANG Zaitang. Random Attractors for Chemostat Model with Wall Attachment Driven by Fractional Brownian Motion[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 61 -68 .
[6]	LI Pengbo, LI Yongxiang. Radial Symmetric Solutions of p-Laplace Equations on Exterior Domains[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 69 -75 .
[7]	WU Zixian, CHENG Jun, FU Jianling, ZHOU Xinwen, XIE Jialong, NING Quan. Analysis of PI-based Event-Triggered Control Design for Semi-Markovian Power Systems[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 76 -85 .
[8]	CHENG Lei, YAN Puxuan, DU Bohao, YE Si, ZOU Huahong. Thermal Stability and Dielectric Relaxation of MOF-2 Synthesized in Aqueous Phase[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 86 -95 .
[9]	LIU Meiyu, ZHANG Jinyan, ZHOU Tongxi, LIAO Guangfeng, YANG Xinzhou, LU Rumei. A New C₂₁ Steroidal Glycoside from Gymnema sylvestre and Its Hypoglycemic Activity[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 96 -104 .
[10]	WANG Wei, DENG Hua, HU Lening, LI Yang. Adsorption Performance of Red Mud-Sodium Alginate Hydrogel on Pb(Ⅱ) in Water[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 105 -115 .

Point Cloud Classification Based on Residual Dynamic Graph Convolution and Feature Enhancement

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 10

Metrics

Comments

Recommended 10

[1]	ZOU Yanli, WANG Yang, LIU Shusheng, YAO Fei. Study of Cascading Failure in the Grid under the Capacity Load Model with Neighbors Degree Information [J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(4): 27-36.
[2]	FAN Rui, JIANG Pinqun, ZENG Shangyou, XIA Haiying, LIAO Zhixian, LI Peng. Design of Lightweight Convolution Neural Network Based on Multi-scale Parallel Fusion [J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(3): 50-59.
[3]	TANG Tang, WEI Chengyun, LUO Xiaoshu, QIU Senhui. Study of Seeker Optimization Algorithm with Inertia TermSelf-tuning to Attitude Stability of Quadrotor UAV [J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(4): 12-19.
[4]	XU Yuanjing, HU Weiping. Identification of Pathological Voice of Different Levels Based on Random Forest [J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(4): 34-41.
[5]	ZHONG Haixin, QIU Senhui, LUO Xiaoshu, TANG Tang, YANG Li, ZHAO Shuai. Study of Applying BP Neural Network with Inertia Term Self-tuningto Attitude Stability of Quadrotor Unmanned Aerial Vehicle [J]. Journal of Guangxi Normal University(Natural Science Edition), 2017, 35(2): 24-31.
[6]	ZHONG Haixin, LUO Xiaoshu, ZHAO Shuai, YANG Li, TANG Tang. Research on Attitude Stability Control of Quadrotor Unmanned Aerial Vehicle Based on Improved Elitist Ant Colony System Algorithm [J]. Journal of Guangxi Normal University(Natural Science Edition), 2016, 34(4): 85-92.
[7]	CHEN Jin, LUO Xiaoshu. A Novel QPSK Signal Demodulation Method Based on Wavelet Transform [J]. Journal of Guangxi Normal University(Natural Science Edition), 2016, 34(2): 35-45.
[8]	ZHANG Jun-jian, YANG Xiu-qin. Minimum Weighted KS Estimate [J]. Journal of Guangxi Normal University(Natural Science Edition), 2012, 30(4): 54-58.
[9]	XIONG Shun-qing, ZHOU Wei-hong. New Digital Watermarking Algorithm Based on NSCT and SVD [J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(2): 195-199.
[10]	LI Yong, JIA Zhen. Applications of Discrete Chaotic Systems in Secure Communication [J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(1): 15-19.