{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T20:38:45Z","timestamp":1792701525314,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"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":["52175051"],"award-info":[{"award-number":["52175051"]}],"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":["52375054"],"award-info":[{"award-number":["52375054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The operational environment of construction machinery is predominantly unstructured, characterized by rapid changes, high complexity, and irregularly distributed objects. This poses significant challenges for 3D semantic perception, particularly due to the high cost of acquiring point cloud semantic labels. To address this, a novel 3D semantic perception scheme is proposed for such unstructured environments. This scheme integrates image semantic segmentation results with point cloud clustering via perspective projection. The projection parameters are refined using Particle Swarm Optimization (PSO), and the semantic consistency of the fused results is further enhanced by a Kd-tree-based radius nearest neighbor (RNN) matching algorithm. Consequently, a weakly supervised framework is established that achieves accurate 3D semantic understanding using only 2D image labels, eliminating the need for annotated 3D point clouds. The feasibility and effectiveness of the scheme are validated through a dedicated unstructured scene dataset and real-world testing. Results demonstrate its capability to effectively perceive 3D semantic information and reconstruct target contours, achieving a mean Pixel Accuracy (mPA) of 84.72% and a mean Intersection over Union (mIoU) of 75.85%.<\/jats:p>","DOI":"10.3390\/s26041257","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:38:39Z","timestamp":1771231119000},"page":"1257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semantic Segmentation and Effect Optimization of 3D Point Cloud Based on 2D Semantic Segmentation and Clustering for Construction Machinery Unstructured Environment"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1155-9451","authenticated-orcid":false,"given":"Shengjie","family":"Fu","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qipeng","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongshen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8571-7988","authenticated-orcid":false,"given":"Tianliang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qihuai","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6752-5544","authenticated-orcid":false,"given":"Zhaoyuan","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100130","DOI":"10.1016\/j.ese.2021.100130","article-title":"Towards carbon neutrality and China\u2019s 14th Five-Year Plan: Clean energy transition, sustainable urban development, and investment priorities","volume":"8","author":"Hepburn","year":"2021","journal-title":"Environ. Sci. Ecotechnol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3904","DOI":"10.1109\/TMECH.2025.3546299","article-title":"Multi-Modal Working Environment Perception for Autonomous Excavation of Mining Electric Shovel Based on Parallel Neural Network","volume":"30","author":"Yao","year":"2025","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1504\/IJHM.2024.135994","article-title":"Obstacle detection for intelligent robots based on the fusion of 2D lidar and depth camera","volume":"7","author":"Fan","year":"2024","journal-title":"Int. J. Hydromechatronics"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cao, P., Chen, H., Zhang, Y., and Wang, G. (2019). Multi-View Frustum Pointnet for Object Detection in Autonomous Driving. 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22\u201325 September 2019, IEEE.","DOI":"10.1109\/ICIP.2019.8803572"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Vora, S., Lang, A.H., Helou, B., and Beijbom, O. (2020). PointPainting: Sequential Fusion for 3D Object Detection. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13\u201319 June 2020, IEEE.","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"ref_6","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. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21\u201326 July 2017, IEEE.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Zhou, Y., and Tuzel, O. (2019). MVX-Net: Multimodal VoxelNet for 3D Object Detection. 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20\u201324 May 2019, IEEE.","DOI":"10.1109\/ICRA.2019.8794195"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pang, S., Morris, D., and Radha, H. (2020). CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 25\u201329 October 2020, IEEE.","DOI":"10.1109\/IROS45743.2020.9341791"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., and Tan, M. (2021). Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10\u201317 October 2021, IEEE.","DOI":"10.1109\/ICCV48922.2021.01597"},{"key":"ref_10","unstructured":"Zunair, H., and Hamza, A.B. (2022). Masked supervised learning for semantic segmentation. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2015.10.019","article-title":"Graph regularized sparse coding for 3D shape clustering","volume":"92","author":"Hamza","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_12","first-page":"1942","article-title":"Particle swarm optimization","volume":"Volume 4","author":"Kennedy","year":"1995","journal-title":"ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia, 27 November\u20131 December 1995"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1061\/(ASCE)0733-9364(2008)134:10(776)","article-title":"Areas of application for 3D and 4D models on construction projects","volume":"134","author":"Hartmann","year":"2008","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1080\/09613210600942218","article-title":"Supporting the constructability review with 3D\/4D models","volume":"35","author":"Hartmann","year":"2007","journal-title":"Build. Res. Inf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"052051","DOI":"10.1088\/1755-1315\/252\/5\/052051","article-title":"Distance Measurement System Based on Binocular Stereo Vision","volume":"252","author":"Sun","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for autonomous driving? The kitti vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16\u201321 June 2012, IEEE.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., and Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. Computer Vision\u2014ECCV 2018, PT XIV, Munich, Germany, 8\u201313 September 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yao, Y., Li, Y., and Qin, T. (2023). A Self-organized Maps Ground Extract Method based on Principal Component Analysis. 2023 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Seattle, WA, USA, 27 June\u20131 July 2023, IEEE.","DOI":"10.1109\/AIM46323.2023.10196193"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, L., Li, Y., Li, X., Meng, Z., and Luo, H. (2022). Efficient plane extraction using normal estimation and RANSAC from 3D point cloud. Comput. Stand. Interfaces, 82.","DOI":"10.1016\/j.csi.2021.103608"},{"key":"ref_20","first-page":"638","article-title":"Negative obstacle detection in unstructured environment based on multiple LiDARs and compositional features","volume":"39","author":"Liu","year":"2017","journal-title":"Robot"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"052024","DOI":"10.1088\/1757-899X\/569\/5\/052024","article-title":"An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index","volume":"569","author":"Wang","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.18421\/TEM114-55","article-title":"Distance Analysis Measuring for Clustering using K-Means and Davies Bouldin Index Algorithm","volume":"11","author":"Idrus","year":"2022","journal-title":"TEM J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, F. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Computer Vision\u2014ECCV 2018, PT VII, Munich, Germany, 8\u201313 September 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4204","DOI":"10.1109\/TVT.2019.2895651","article-title":"Dual Cross-Entropy Loss for Small-Sample Fine-Grained Vehicle Classification","volume":"68","author":"Li","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., Sun, X., Meng, Y., Liang, J., Wu, F., and Li, J. (2020). Dice Loss for Data-imbalanced NLP Tasks. Proceedings of the 58th Annual Meeting of the Association-for-Computational-Linguistics (ACL 2020), Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.45"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18\u201323 June 2018, IEEE.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/TITS.2020.3014044","article-title":"Enhancing the Performance of Flow Classification in SDN-Based Intelligent Vehicular Networks","volume":"22","author":"Abbasi","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"011013","DOI":"10.1117\/1.JEI.26.1.011013","article-title":"Four-dimensional reconstruction of cultural heritage sites based on photogrammetry and clustering","volume":"26","author":"Voulodimos","year":"2017","journal-title":"J. Electron. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., and Stachniss, C. (2019). Rangenet++: Fast and accurate lidar semantic segmentation. 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3\u20138 November 2019, IEEE.","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020). Randla-net: Efficient semantic segmentation of large-scale point clouds. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13\u201319 June 2020, IEEE.","DOI":"10.1109\/CVPR42600.2020.01112"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1257\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:40:39Z","timestamp":1771231239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,14]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26041257"],"URL":"https:\/\/doi.org\/10.3390\/s26041257","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,14]]}}}