{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T09:41:25Z","timestamp":1778578885901,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T00:00:00Z","timestamp":1585008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671434"],"award-info":[{"award-number":["41671434"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51574242"],"award-info":[{"award-number":["51574242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Water Resources Protection and Utilization in Coal Mining independent Research Project","award":["GJNY-18-77"],"award-info":[{"award-number":["GJNY-18-77"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the sparse point cloud. This paper proposes a lightweight auxiliary network, called the rotated density-based network (RD-Net), and a novel point cloud preprocessing method, Grid Trajectory Box (GT-Box), to solve these problems. The combination of RD-Net and PointNet was used to achieve high-precision 3D classification and segmentation of the sparse point cloud. It emphasizes the importance of the density feature of LiDAR points for 3D object recognition of sparse point cloud. Furthermore, RD-Net plus PointCNN, PointNet, PointCNN, and RD-Net were introduced as comparisons. Public datasets were used to evaluate the performance of the proposed method. The results showed that the RD-Net could significantly improve the performance of sparse point cloud recognition for the coordinate-based network and could improve the classification accuracy to 94% and the segmentation per-accuracy to 70%. Additionally, the results concluded that point-density information has an independent spatial\u2013local correlation and plays an essential role in the process of sparse point cloud recognition.<\/jats:p>","DOI":"10.3390\/ijgi9030182","type":"journal-article","created":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T07:16:08Z","timestamp":1585034168000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Classification and Segmentation of Mining Area Objects in Large-Scale Spares Lidar Point Cloud Using a Novel Rotated Density Network"],"prefix":"10.3390","volume":"9","author":[{"given":"Yueguan","family":"Yan","sequence":"first","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining &amp; Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixu","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining &amp; Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junting","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huayang","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining &amp; Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, W., Sakurada, K., and Kawaguchi, N. (2017). Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard. Remote Sens., 9.","DOI":"10.3390\/rs9080851"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"B\u0142aszczak-B\u0105k, W., Suchocki, C., Janicka, J., Dumalski, A., Duchnowski, R., and Sobieraj-\u017b\u0142obi\u0144ska, A. (2020). Automatic Threat Detection for Historic Buildings in Dark Places Based on the Modified OptD Method. Isprs Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020123"},{"key":"ref_3","first-page":"40","article-title":"Whole Object Deformation Monitoring Based on 3D Laser Scanning Technology","volume":"7","author":"Luo","year":"2005","journal-title":"Bull. Surv. Mapp."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1080\/01431161.2014.1001086","article-title":"Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation","volume":"36","author":"Maxwell","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15917","DOI":"10.3390\/rs71215811","article-title":"Object-based canopy gap segmentation and classification: Quantifying the pros and cons of integrating optical and LiDAR data","volume":"7","author":"Yang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fu, K., Dai, W., Zhang, Y., Wang, Z., Yan, M., and Sun, X. (2019). MultiCAM: Multiple Class Activation Mapping for Aircraft Recognition in Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11050544"},{"key":"ref_7","unstructured":"Chen, X., Kohlmeyer, B., Stroila, M., Alwar, N., Wang, R., and Bach, J. (2019, January 5\u20138). Next generation map making: Geo-referenced ground-level LIDAR point clouds for automatic retro-reflective road feature extraction. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"807","DOI":"10.5558\/tfc84807-6","article-title":"The role of LiDAR in sustainable forest management","volume":"84","author":"Wulder","year":"2008","journal-title":"For. Chron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Liu, L., Li, X., Sangaiah, A.K., and Li, K. (2018). Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data. Remote Sens., 10.","DOI":"10.3390\/rs10081222"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., and Jia, J. (2018, January 8\u201314). Icnet for real-time semantic segmentation on high-resolution images. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, B., Jiang, W., Shan, J., Zhang, J., and Li, L. (2016). Investigation on the weighted ransac approaches for building roof plane segmentation from lidar point clouds. Remote Sens., 8.","DOI":"10.3390\/rs8010005"},{"key":"ref_12","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., and Chen, B. (2018). PointCNN: Convolution on X-Transformed Points. NeurIPS, 828\u2013838."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/LGRS.2017.2764938","article-title":"Tree Classification in Complex Forest Point Clouds Based on Deep Learning","volume":"14","author":"Zou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020). PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3892","DOI":"10.1109\/JSTARS.2018.2869542","article-title":"Voxel-based extraction of transmission lines from airborne LiDAR point cloud data","volume":"11","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Heidemann, H.K. (2012). Lidar Base Specification (No. 11-B4).","DOI":"10.3133\/tm11B4"},{"key":"ref_18","unstructured":"Tatebe, Y., Deguchi, D., Kawanishi, Y., Ide, I., Murase, H., and Sakai, U. (March, January 27). Can we detect pedestrians using low-resolution LIDAR?. Proceedings of the Computer Vision Theory and Applications, Porto, Portugal."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tatebe, Y., Deguchi, D., Kawanishi, Y., Ide, I., Murase, H., and Sakai, U. (2018, January 7\u20139). Pedestrian detection from sparse point-cloud using 3DCNN. Proceedings of the 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand.","DOI":"10.1109\/IWAIT.2018.8369680"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/34.121785","article-title":"Structural indexing: Efficient 3-d object recognition","volume":"14","author":"Stein","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tuzel, O., Liu, M.-Y., Taguchi, Y., and Raghunathan, A. (2014, January 6\u201312). Learning to rank 3d features. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland. Proceedings, Part I.","DOI":"10.1007\/978-3-319-10590-1_34"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gonzalez, A., Villalonga, G., Xu, J., Vazquez, D., Amores, J., and Lopez, A. (2015). Multiview random forest of local experts combining rgb and lidar data for pedestrian detection. IEEE Intell. Veh. Symp. (IV), 356\u2013361.","DOI":"10.1109\/IVS.2015.7225711"},{"key":"ref_23","unstructured":"Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., and Urtasun, R. (2015). 3d object proposals for accurate object class detection. Adv. Neural Inf. Process. Syst. (Nips), 424\u2013432."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Song, S., and Xiao, J. (2016). Deep Sliding Shapes for amodal 3D object detection in RGB-D images. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 808\u2013816.","DOI":"10.1109\/CVPR.2016.94"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017). Multi-view 3D Object Detection Network for Autonomous Driving. IEEE Conf. Comput. Vis. Pattern Recognit. (Cvpr), 6526\u20136534.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conf. Comput. Vis. Pattern Recognit. (Cvpr), 77\u201385.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_27","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst., 5099\u20135108."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cheraghian, A., Rahman, S., and Petersson, L. (2019). Zero-shot learning of 3d point cloud objects. Int. Conf. Mach. Vis. Appl. (Mva). IEEE, 1\u20136.","DOI":"10.23919\/MVA.2019.8758063"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B.M., and Lee, G.H. (2018). So-net: Self-organizing network for point cloud analysis. Proc. Ieee Conf. Comput. Vis. Pattern Recognit., 9397\u20139406.","DOI":"10.1109\/CVPR.2018.00979"},{"key":"ref_30","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph. (Tog)"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cheraghian, A., Rahman, S., and Petersson, L. (2019, January 27\u201331). Zero-shot learning of 3d point cloud objects. Proceedings of the 2019 16th International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.23919\/MVA.2019.8758063"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ulusoy, A.O., and Geiger, A. (2017). OctNet: Learning Deep 3D Representations at High Resolutions. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 808\u2013816.","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (2015). VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. IEEE\/Rsj Int. Conf. Intell. Robot. Syst. (IROS), 922\u2013928.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 18\u201322). Voxelnet: End-to-end learning for point cloud based 3d object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_35","first-page":"10","article-title":"Voting for voting in online point cloud object detection","volume":"1","author":"Wang","year":"2015","journal-title":"Proc. Robot. Sci. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Engelcke, M., Rao, D., Wang, D.Z., Tong, C.H., and Posner, I. (2017). Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks. IEEE Int. Conf. Robot. Autom. (ICRA), 1355\u20131361.","DOI":"10.1109\/ICRA.2017.7989161"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2019, January 16\u201320). Pointpillars: Fast encoders for object detection from point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). Multi-view 3d object detection network for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3980","DOI":"10.1109\/TCYB.2016.2593940","article-title":"Onboard object detection: Multicue, multimodal, and multiview random forest of local experts","volume":"47","author":"Gonzlez","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1109\/TIP.2011.2142006","article-title":"A multilevel mixture-ofexperts framework for pedestrian classification","volume":"20","author":"Enzweiler","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). Scannet: Richly-annotated 3d reconstructions of indoor scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_42","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., and Pollefeys, M. (2017). Semantic3d. net: A new large-scale point cloud classification benchmark. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/3\/182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:02Z","timestamp":1760173862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/3\/182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,24]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["ijgi9030182"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9030182","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,24]]}}}