计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (07): 1239-1246.
吴亦奇1,2,韩放1,张德军1,何发智3,陈壹林4
收稿日期:
2021-11-24
修回日期:
2022-02-05
接受日期:
2022-07-25
出版日期:
2022-07-25
发布日期:
2022-07-25
基金资助:
WU Yi-qi1,2,HAN Fang1,ZHANG De-jun1,HE Fa-zhi3,CHEN Yi-lin4
Received:
2021-11-24
Revised:
2022-02-05
Accepted:
2022-07-25
Online:
2022-07-25
Published:
2022-07-25
摘要: 点云模型的分类与部件分割是三维点云数据处理的基本任务,其核心在于获取可以有效表示三维模型的点云特征。提出一个引入注意力机制的三维点云特征学习网络。该网络采用多层次点云特征提取方法,首先使用特征通道注意力模块获取各通道间的关联,增强关键通道信息; 接着引入空间位置注意力机制,基于点的空间位置信息获取各点的注意力权重;然后结合以上2种注意力机制获取增强的点云特征;最后基于该特征继续进行多层次特征提取,获得面向下游任务的点云特征。分别在ModelNet40和ShapeNet数据集上进行形状分类与部件分割实验,结果表明,使用所提方法可以实现高精度、具有鲁棒性的三维点云形状分类与分割。
吴亦奇, 韩放, 张德军, 何发智, 陈壹林. 基于特征通道和空间位置注意力的三维点云特征学习网络[J]. 计算机工程与科学, 2022, 44(07): 1239-1246.
WU Yi-qi, HAN Fang, ZHANG De-jun, HE Fa-zhi, CHEN Yi-lin. A 3D point cloud feature learning network based on feature channel and spatial position attentions[J]. Computer Engineering & Science, 2022, 44(07): 1239-1246.
[1] | Guo Y,Sohel F,Bennamoun M,et al.Rotational projection statistics for 3D local surface description and object recognition [J].International Journal of Computer Vision,2013,105(1):63-86. |
[2] | Guo Y,Bennamoun M,Sohel F,et al.3D object recognition in cluttered scenes with local surface features:A survey [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(11):2270-2287. |
[3] | Minaee S,Boykov Y Y,Porikli F,et al.Image segmentation using deep learning:A survey [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,44(7):1. |
[4] | Chan T-H,Jia K,Gao S,et al.PCANet:A simple deep learning baseline for image classification? [J].IEEE Transactions on Image Processing,2015,24(12):5017-5032. |
[5] | Guo Y,Wang H,Hu Q,et al.Deep learning for 3D point clouds:A survey [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,43(12):4338-4364. |
[6] | Ioannidou A,Chatzilari E,Nikolopoulos S,et al.Deep learning advances in computer vision with 3D data:A survey [J].ACM Computing Surveys (CSUR),2017,50(2):1-38. |
[7] | Qi C R,Su H,Nieβner M,et al.Volumetric and multi-view CNNs for object classification on 3D data[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2016:5648-5656. |
[8] | Su H,Maji S,Kalogerakis E,et al.Multi-view convolutional neural networks for 3D shape recognition[C]∥Proc of the IEEE International Conference on Computer Vision,2015:945-953. |
[9] | Chen X,Ma H,Wan J,et al.Multi-view 3D object detection network for autonomous driving[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2017:1907-1915. |
[10] | Yu T,Meng J,Yuan J.Multi-view harmonized bilinear network for 3D object recognition[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2018:186-194. |
[11] | Yang Z,Wang L.Learning relationships for multi-view 3D object recognition[C]∥Proc of the IEEE/CVF International Conference on Computer Vision,2019:7505-7514. |
[12] | Maturana D, Scherer S.VoxNet:A 3D convolutional neural network for real-time object recognition[C]∥Proc of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2015:922-928. |
[13] | Riegler G, Osman Ulusoy A, Geiger A. OctNet:Learning deep 3D representations at high resolutions[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2017:3577-3586. |
[14] | 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 (TOG),2017,36(4):1-11. |
[15] | Le T,Duan Y.PointGrid:A deep network for 3D shape understanding[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2018:9204-9214. |
[16] | Qi C R,Su H,Mo K,et al.PointNet:Deep learning on point sets for 3D classification and segmentation[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2017:652-660. |
[17] | Qi C R,Yi L,Su H,et al.PointNet++:Deep hierarchical feature learning on point sets in a metric space[C]∥Proc of the 31st International Conference on Neural Information Processing Systems,2017:5105-5114. |
[18] | Duan Y,Zheng Y,Lu J,et al.Structural relational reasoning of point clouds[C]∥Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:949-958. |
[19] | Yang J,Zhang Q,Ni B,et al.Modeling point clouds with self-attention and gumbel subset sampling[C]∥Proc of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:3318-3327. |
[20] | Bai Jing, Si Qing-long,Qin Fei-wei.Lightweight real-time point cloud classification network LightPointNet[J].Journal of Computer-Aided Design & Graphics,2019,31(4):612-621.(in Chinese) |
[21] | Zhao H,Jiang L,Fu C-W,et al.PointWeb:Enhancing local neighborhood features for point cloud processing[C]∥Proc of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:5560-5568. |
[22] | Xu Y,Fan T,Xu M,et al.SpiderCNN:Deep learning on point sets with parameterized convolutional filters[C]∥Proc of the European Conference on Computer Vision (ECCV),2018:87-102. |
[23] | Wu W X,Qi Z A, Li F X.PointConv:Deep convolutional networks on 3D point clouds[C]∥Proc of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:9621-9630. |
[24] | Li Y, Bu R,Sun M,et al.PointCNN:Convolution on x-transformed points [C]∥Proc of Annual Conference on Neural Information Processing Systems,2018:828-838. |
[25] | Peyghambarzadeh S M,Azizmalayeri F,Khotanlou H,et al.Point-PlaneNet:Plane kernel based convolutional neural network for point clouds analysis [J].Digital Signal Processing,2020,98:102633. |
[26] | Thomas H, Qi C R,Deschaud J-E,et al.KPconv:Flexible and deformable convolution for point clouds[C]∥Proc of 2019 IEEE/CVF International Conference on Computer Vision,2019:6410-6419. |
[27] | Shen Y, Feng C,Yang Y,et al.Mining point cloud local structures by kernel correlation and graph pooling[C]∥Proc of 2018 IEEE Conference on Computer Vision and Pattern Recognition,2018:4548-4557. |
[28] | Wang C,Samari B,Siddiqi K.Local spectral graph convolution for point set feature learning[C]∥Proc of the European Conference on Computer Vision (ECCV),2018:52-66. |
[29] | Wang Y,Sun Y,Liu Z,et al.Dynamic graph CNN for learning on point clouds [J].ACM Transactions on Graphics,2019,38(5):1-12. |
[30] | Vaswani A,Shazeer N,Parmar N,et al.Attention is all you need[C]∥Proc of the 31st International Conference on Neural Information Processing Systems,2017:5998-6008. |
[31] | Lü Fan, Hu Fu-yuan,Zhang Yan-ning,et al.Feedback attention model for image captioning [J].Journal of Computer-Aided Design & Graphics,2019,31(7):1122-1129.(in Chinese) |
[32] | Jia Ning,Zheng Chun-jun.Model of music theme recommendation based on attention LSTM [J].Computer Science,2019,46(S2):230-235.(in Chinese) |
[33] | Huang Huan,Sun Li-juan,Cao Ying,et al.Multimodal sentiment analysis of short videos based on attention [J].Journal of Graphics,2021,42(1):8-14.(in Chinese) |
[34] | Chen Qing-wen,Xie Hong-wen,Zha Hao,et al.Salient object detection based on deep clustering attention mechanism [J].Journal of Image and Graphics,2021,26(5):1017-1029.(in Chinese) |
[35] | Li Xuan-ye,Hao Xing-wei,Jia Jin-gong,et al.Human action recognition method based on multi-attention mechanism and spatiotemporal graph convolution [J].Journal of Computer-Aided Design & Graphics,2021,33(7):1055-1063.(in Chinese) |
[36] | Wu Z,Song S,Khosla A,et al.3D ShapeNets:A deep representation for volumetric shapes[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2015:1912-1920. |
[37] | Li J,Chen B M,Lee G H.SO-Net:Self-organizing network for point cloud analysis[C]∥Proc of the IEEE Conference on Computer Vision and Pattern Recognition,2018:9397-9406. |
[38] | Klokov R, Lempitsky V. Escape from cells:Deep kd- networks for the recognition of 3D point cloud models[C]∥Proc of 2017 IEEE International Conference on Computer Vision,2017:863-872. |
[39] | He X,Cao H L,Zhu B.AdvectiveNet:An Eulerian-Lagrangian fluidic reservoir for point cloud processing [J].arXiv:200200118,2020. |
[40] | Guo M H,Cai J X,Liu Z N,et al.PCT:Point cloud transformer [J].Computational Visual Media,2021,7(2):187-199. |
[41] | Qiu S, Anwar S,Barnes N.Dense-resolution network for point cloud classification and segmentation[C]∥Proc of the IEEE/CVF Winter Conference on Applications of Computer Vision,2021:3813-3822. |
[42] | 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,2016,35(6):1-12. |
[43] | Huang Q,Wang W,Neumann U.Recurrent slice networks for 3D segmentation of point clouds[C]∥Proc of 2018 IEEE Conference on Computer Vision and Pattern Recognition,2018:2626-2635. |
[44] | Zhang D,He L,Luo M,et al.Weight asynchronous update:Improving the diversity of filters in a deep convolutional network [J].Computational Visual Media,2020,6(4):455-66. |
[45] | Zhang D,He F,Tu Z,et al.Pointwise geometric and semantic learning network on 3D point clouds [J].Integrated Computer-Aided Engineering,2020,27(1):57-75. |
[46] | Zhang D,He L,Tu Z,et al.Learning motion representation for real-time spatio-temporal action localization [J].Pattern Recognition,2020,103:107312. |
[47] | Chen Y,He F,Wu Y,et al.A local start search algorithm to compute exact Hausdorff distance for arbitrary point sets [J].Pattern Recognition,2017,67:139-48. |
[48] | Chen Y L,He F Z, Li H R,et al.A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration [J].Applied Soft Computing,2020,93:106335. |
附中文参考文献: | |
[20] | 白静,司庆龙,秦飞巍.轻量级实时点云分类网络LightPointNet [J].计算机辅助设计与图形学学报,2019,31(4):612-621. |
[31] | 吕凡,胡伏原,张艳宁,等.面向图像自动语句标注的注意力反馈模型 [J].计算机辅助设计与图形学学报,2019,31(7):1122-1129. |
[32] | 贾宁,郑纯军.基于注意力LSTM的音乐主题推荐模型 [J].计算机科学,2019,46(S2):230-235. |
[33] | 黄欢,孙力娟,曹莹,等.基于注意力的短视频多模态情感分析 [J].图学学报,2021,42(1):8-14. |
[34] | 陈庆文,谢宏文,查浩,等.深度聚类注意力机制下的显著对象检测 [J].中国图象图形学报,2021,26(5):1017-1029. |
[35] | 李炫烨,郝兴伟,贾金公,等.结合多注意力机制与时空图卷积网络的人体动作识别方法 [J].计算机辅助设计与图形学学报,2021,33(7):1055-1063. |
[1] | 马思远, 焦佳辉, 任晟岐, 宋伟. 基于注意力机制的城市多元空气质量数据缺失值填充[J]. 计算机工程与科学, 2023, 45(08): 1354-1364. |
[2] | 尹春勇, 冯梦雪. 基于注意力机制的半监督日志异常检测方法[J]. 计算机工程与科学, 2023, 45(08): 1405-1415. |
[3] | 余子丞, 凌捷. 基于Transformer和多特征融合的DGA域名检测方法[J]. 计算机工程与科学, 2023, 45(08): 1416-1423. |
[4] | 唐剑, 车文刚, 高盛祥. 融入注意力机制的多尺度卷积图像去雾方法[J]. 计算机工程与科学, 2023, 45(08): 1453-1462. |
[5] | 吴栋梁, 刘知贵, . 基于轻量化YOLOX的电子元器件缺陷检测方法研究[J]. 计算机工程与科学, 2023, 45(08): 1463-1471. |
[6] | 刘浩翰, 孙铖, 贺怀清, 惠康华. 基于改进YOLOv3的金属表面缺陷检测[J]. 计算机工程与科学, 2023, 45(07): 1226-1235. |
[7] | 白杉, 冯秀芳. 基于注意力增强的中心差分自适应图卷积的骨架行为识别[J]. 计算机工程与科学, 2023, 45(07): 1263-1273. |
[8] | 王剑, 姜林, 王琳钦, 余正涛, 张松, 高盛祥, . 基于BiLSTM的低资源老挝语文本正则化任务[J]. 计算机工程与科学, 2023, 45(07): 1292-1299. |
[9] | 田秀霞, 刘正, 刘秋旭, 李浩然. 一种改进Faster R-CNN的图像篡改检测模型[J]. 计算机工程与科学, 2023, 45(06): 1030-1039. |
[10] | 杨慧剑, 孟亮. 基于改进的YOLOv5的航拍图像中小目标检测算法[J]. 计算机工程与科学, 2023, 45(06): 1063-1070. |
[11] | 刘晓航, 姜晶菲, 许金伟. 基于脉动阵列的层融合注意力模型加速器结构[J]. 计算机工程与科学, 2023, 45(05): 802-809. |
[12] | 排日旦·阿布都热依木, 吐尔地·托合提, 艾斯卡尔·艾木都拉, . 基于深度学习的实体关系抽取方法研究[J]. 计算机工程与科学, 2023, 45(05): 895-902. |
[13] | 赵乐乐, 张丽萍, 赵凤荣. 基于注意力机制的Tree2Seq代码注释自动生成[J]. 计算机工程与科学, 2023, 45(04): 638-645. |
[14] | 厍向阳, 马亦骏. 改进的遥感图像语义分割算法[J]. 计算机工程与科学, 2023, 45(03): 504-511. |
[15] | 张若一, 金柳, 马慧芳, 王亦可, 李清风. 融合相似用户影响效应的知识图谱推荐模型[J]. 计算机工程与科学, 2023, 45(03): 520-527. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
湘公网安备 43010502000083号
湘ICP备10006030号
版权所有 © 《计算机工程与科学》 编辑部
地址:中国湖南省长沙市开福区德雅路109号(410073) 电话:0731-87002567 Email: jsjgcykx@vip.163.com
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn