大规模点云直线段的提取
Line Segment Extraction from Large-Scale Point Clouds
DOI: 10.12677/GST.2022.102008, PDF,    国家自然科学基金支持
作者: 胡宗田*, 陈 驰：武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉；杨必胜：武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉；武汉大学时空数据智能获取技术与应用教育部工程研究中心，湖北 武汉；张 飞：上海维智卓新信息科技有限公司，上海
关键词: 三维直线段提取；激光扫描；特征提取；点云；3D Line Segment Extraction； Laser Scanning； Feature Extraction； Point Clouds
摘要: 三维直线段作为最常见的基元，在人工场景的矢量重建中发挥着重要作用。本文提出了一种从大规模点云中三维直线段提取的方法。该方法首先通过区域增长和区域合并提取了三维投影平面，然后将其投影成二维图像，最后将投影图像中提取的二维直线段反投影到三维空间得到了三维直线段。大规模户外场景的点云数据集实验表明，所提出的方法可精准地提取场景中的直线特征线段，同时过滤掉大部分的噪声，从而能够提取更完整的直线段。与人工标注的真值直线段相比( : 0.5, 0.5)相比，本文提出的方法提取的完成率和准确率平均达到83%，而且平均处理速度为每秒27,000点。
Abstract: As the most common primitives, line segments play an essential role in the vectorized reconstruction of artificial scenes. In this paper, a method is proposed to extract line segments from large-scale point clouds. This method firstly extracts the 3D projection plane through region growing and region merging, then projects it to 2D to form an image, and finally back-projects the 2D line segment extracted from the projected image to 3D to obtain a 3D line segment. Experiments on point clouds datasets of large-scale outdoor scenes show that the proposed method can accurately extract the linear feature line segments representing the scene, filter out major nosie, and extract more complete line segments. The Completeness and Correctness of the line segments extraction results reach 83% on average compared with manual labeled ground truth ( : 0.5, 0.5), at an average processing speed of 27,000 points per second.
文章引用：胡宗田, 杨必胜, 陈驰, 张飞. 大规模点云直线段的提取[J]. 测绘科学技术, 2022, 10(2): 69-80. https://doi.org/10.12677/GST.2022.102008

参考文献

[1] 杨必胜, 董震. 点云智能研究进展与趋势[J]. 测绘学报, 2019, 48(12): 1575-1585.
[2] 杨必胜, 董震. 点云智能处理[M]. 北京: 科学出版社, 2020: 31-36.
[3] Yang, B. and Chen, C. (2015) Automatic Registration of UAV-Borne Sequent Images and LiDAR Data. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 262-274. [Google Scholar] [CrossRef] 
[4] Dong, Z., Liang, F., Yang, B., Xu, Y., Zang, Y., Li, J., Wang, Y., Dai, W., Fan, H., Hyyppa, J. and Stilla, U. (2020) Registration of Large-Scale Terrestrial Laser Scanner Point Clouds: A Review and Benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 163, 327-342. [Google Scholar] [CrossRef] 
[5] Chen, C. and Yang, B. (2016) Dynamic Occlusion Detection and Inpainting of in Situ Captured Terrestrial Laser Scanning Point Clouds Sequence. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 90-107. [Google Scholar] [CrossRef] 
[6] Han, X., Dong, Z. and Yang, B. (2021) A Point-Based Deep Learning Network for Semantic Segmentation of MLS Point Clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 199-214. [Google Scholar] [CrossRef] 
[7] Yang, B., Dong, Z., Zhao, G. and Dai, W. (2015) Hierarchical Extraction of Urban Objects from Mobile Laser Scanning Data. ISPRS Journal of Photogrammetry and Remote Sensing, 99, 45-57. [Google Scholar] [CrossRef] 
[8] Chen, C., Yang, B., Song, S., Peng, X. and Huang, R. (2018) Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data. Remote Sensing, 10, Article ID: 613. [Google Scholar] [CrossRef] 
[9] Xu, Y. and Stilla, U. (2021) Towards Building and Civil Infrastructure Reconstruction from Point Clouds: A Review on Data and Key Techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2857-2885. [Google Scholar] [CrossRef] 
[10] Xia, S., Chen, D., Wang, R., Li, J. and Zhang, X. (2020) Geometric Primitives in LiDAR Point Clouds: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 685-707. [Google Scholar] [CrossRef] 
[11] Yuan, C., Liu, X., Hong, X. and Zhang, F. (2021) Pixel-Level Extrinsic Self Calibration of High Resolution Lidar and Camera in Targetless Environments. IEEE Robotics and Automation Letters, 6, 7517-7524. [Google Scholar] [CrossRef] 
[12] Yu, H., Zhen, W., Yang, W., Zhang, J. and Scherer, S. (2020) Monocular Camera Localization in Prior Lidar Maps with 2D-3D Line Correspondences. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, 24 October-24 January 2021, 4588-4594. [Google Scholar] [CrossRef] 
[13] Yu, H., Zhen, W., Yang, W. and Scherer, S. (2020) Line-Based 2-D-3-D Registration and Camera Localization in Structured Environments. IEEE Transactions on Instrumentation and Measurement, 69, 8962-8972. [Google Scholar] [CrossRef] 
[14] Li, S., Ge, X., Li, S., Xu, B. and Wang, Z. (2021) Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds. Remote Sensing, 13, Article No. 2195. [Google Scholar] [CrossRef] 
[15] Amblard, V., Osedach, T.P., Croux, A., Speck, A. and Leonard, J.J. (2021) Lidar-Monocular Surface Reconstruction Using Line Segments. 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, 30 May-5 June 2021, 5631-5637. [Google Scholar] [CrossRef] 
[16] Cui, Y., Li, Q. and Dong, Z. (2019) Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds. Remote Sensing, 11, Article No. 2262. [Google Scholar] [CrossRef] 
[17] Brown, M., Windridge, D. and Guillemaut, J.Y. (2015) A Generalisable Framework for Saliency-Based Line Segment Detection. Pattern Recognition, 48, 3993-4011. [Google Scholar] [CrossRef] 
[18] Meng, Q., Zhang, J., Hu, Q., He, X. and Yu, J. (2020) LGNN: A Context-Aware Line Segment Detector. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, 12-16 October 2020, 4364-4372. [Google Scholar] [CrossRef] 
[19] Lin, Y., Wang, C., Cheng, J., Chen, B., Jia, F., Chen, Z. and Li, J. (2015) Line Segment Extraction for Large Scale Unorganized Point Clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 172-183. [Google Scholar] [CrossRef] 
[20] Lu, X., Liu, Y. and Li, K. (2019) Fast 3D Line Segment Detection from Unorganized Point Cloud. arXiv preprint arXiv:1901.02532.
[21] Ni, H., Lin, X., Ning, X. and Zhang, J. (2016) Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods. Remote Sensing, 8, Article No. 710. [Google Scholar] [CrossRef] 
[22] Xia, S. and Wang, R. (2017) A Fast Edge Extraction Method for Mobile LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters, 14, 1288-1292. [Google Scholar] [CrossRef] 
[23] Hackel, T., Wegner, J.D. and Schindler, K. (2016) Contour Detection in Unstructured 3D Point Clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 1610-1618. [Google Scholar] [CrossRef] 
[24] Yu, L., Li, X., Fu, C.W., Cohen-Or, D. and Heng, P.-A. (2018) EC-Net: An Edge-Aware Point Set Consolidation Network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, 8-14 September 2018, 386-402. [Google Scholar] [CrossRef] 
[25] Zhang, W., Chen, L., Xiong, Z., Zang, Y., Li, J. and Zhao, L. (2020) Large-Scale Point Cloud Contour Extraction via 3D Guided Multi-Conditional Generative Adversarial Network. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 97-105. [Google Scholar] [CrossRef] 
[26] Wang, X., Xu, Y., Xu, K., Tagliasacchi, A., Zhou, B., Mahdavi-Amiri, A. and Zhang, H. (2020) Pie-Net: Parametric Inference of Point Cloud Edges. Advances in Neural Information Processing Systems, 33, 20167-20178. [Google Scholar] [CrossRef] 
[27] Poma, X.S., Riba, E. and Sappa, A. (2020) Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass, 1-5 March 2020, 1923-1932. [Google Scholar] [CrossRef] 
[28] Elder, J.H., Almazàn, E.J., Qian, Y. and Tal, R. (2020) MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection. arXiv Preprint arXiv:2001.01788.

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