{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:03:11Z","timestamp":1760151791956,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Liaoning Province of China","award":["2020-MS-096"],"award-info":[{"award-number":["2020-MS-096"]}]},{"name":"the Foundation of National Natural Science Foundation of China","award":["61973065, 52075531"],"award-info":[{"award-number":["61973065, 52075531"]}]},{"name":"the Fundamental Research Funds for the Central Universities of China","award":["N2126008, N2104008"],"award-info":[{"award-number":["N2126008, N2104008"]}]},{"name":"the Central Government Guides the Local Science and Technology Development Special Fund","award":["2021JH6\/10500129"],"award-info":[{"award-number":["2021JH6\/10500129"]}]},{"name":"Innovative Talents Support Program of Liaoning Provincial Universities","award":["LR2020047"],"award-info":[{"award-number":["LR2020047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The research of object classification and part segmentation is a hot topic in computer vision, robotics, and virtual reality. With the emergence of depth cameras, point clouds have become easier to collect and increasingly important because of their simple and unified structures. Recently, a considerable number of studies have been carried out about deep learning on 3D point clouds. However, data captured directly by sensors from the real-world often encounters severe incomplete sampling problems. The classical network is able to learn deep point set features efficiently, but it is not robust enough when the method suffers from the lack of point clouds. In this work, a novel and general network was proposed, whose effect does not depend on a large amount of point cloud input data. The mutual learning of neighboring points and the fusion between high and low feature layers can better promote the integration of local features so that the network can be more robust. The specific experiments were conducted on the ScanNet and Modelnet40 datasets with 84.5% and 92.8% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks, and has good local feature integration ability. Particularly, it can still maintain 87.4% accuracy when the number of input points is further reduced to 128. The model proposed has bridged the gap between classical networks and point cloud processing.<\/jats:p>","DOI":"10.3390\/s22093209","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:45:21Z","timestamp":1650761121000},"page":"3209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Neural Network for Point Sets Based on Local Feature Integration"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1628-1404","authenticated-orcid":false,"given":"Hao","family":"Chu","sequence":"first","affiliation":[{"name":"School of Robotics and Engineering, Northeastern University, Shenyang 110167, China"}]},{"given":"Zhenquan","family":"He","sequence":"additional","affiliation":[{"name":"School of Robotics and Engineering, Northeastern University, Shenyang 110167, China"}]},{"given":"Shangdong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Robotics and Engineering, Northeastern University, Shenyang 110167, China"}]},{"given":"Chuanwen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Robotics and Engineering, Northeastern University, Shenyang 110167, China"}]},{"given":"Jiyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Queen Mary School of Engineering, Northwestern Polytechnical University, Xi\u2019an 710060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8296-8039","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Robotics and Engineering, Northeastern University, Shenyang 110167, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/LGRS.2018.2817358","article-title":"Photograph LIDAR Registration Methodology for Rock Discontinuity Measurement","volume":"15","author":"Morago","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.compenvurbsys.2019.01.004","article-title":"Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach","volume":"75","author":"Park","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, S., and Lee, D. (2019, January 16\u201320). Point-to-Pose Voting Based Hand Pose Estimation Using Residual Permutation Equivariant Layer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01220"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ren, Z., Wang, L., and Bi, L. (2019). Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment. Sensors, 19.","DOI":"10.3390\/s19132915"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mokhayeri, F., and Granger, E. (2020). A paired sparse representation model for robust face recognition from a single sample. Pattern Recognit., 100.","DOI":"10.1016\/j.patcog.2019.107129"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1109\/TGRS.2017.2769120","article-title":"Deep Learning-Based Classification and Reconstruction of Residential Scenes from Large-Scale Point Clouds","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.optlaseng.2019.06.011","article-title":"High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm","volume":"122","author":"Chen","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, F., Duan, J., Chen, S., Ye, Y., Ai, P., and Yang, Z. (2021). Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.705021"},{"key":"ref_9","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_10","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep Hierarchical Feature Learning on Point Sets in A Metric Space. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1007\/s00371-017-1416-3","article-title":"Point-wise saliency detection on 3D point clouds via covariance descriptors","volume":"34","author":"Guo","year":"2018","journal-title":"Vis. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s00138-019-01027-7","article-title":"3D object recognition from cluttered and occluded scenes with a compact local feature","volume":"30","author":"Guo","year":"2019","journal-title":"Mach. Vis. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, F., Liang, C., Ru, C., and Cheng, H. (2019). An Improved Point Cloud Descriptor for Vision Based Robotic Grasping System. Sensors, 19.","DOI":"10.3390\/s19102225"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"226285","DOI":"10.1109\/ACCESS.2020.3044166","article-title":"3D Large-Scale Point Cloud Semantic Segmentation Using Optimal Feature Description Vector Network: OFDV-Net","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Peng, F., Wu, Q., Fan, L., Zhang, J., You, Y., Lu, J., and Yang, J.Y. (2014, January 27\u201330). Street view cross-sourced point cloud matching and registration. Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025406"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, L., Sun, J., and Zheng, Q. (2018). 3D Point Cloud Recognition Based on a Multi-View Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18113681"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qian, G.A., and Xs, B. (2021). ThickSeg: Efficient semantic segmentation of large-scale 3D point clouds using multi-layer projection. Image Vis. Comput., 108.","DOI":"10.1016\/j.imavis.2021.104161"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.robot.2019.03.009","article-title":"Learning 3D local surface descriptor for point cloud images of objects in the real-world","volume":"116","author":"Seo","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Poux, F., and Billen, R. (2019). Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs. Deep Learning Methods. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8050213"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-view Convolutional Neural Networks for 3d Shape Recognition. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_21","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. (2015, January 8\u201310). 3D ShapeNets: A Deep Representation for Volumetric Shapes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_22","unstructured":"Maturana, D., and Scherer, S. (October, January 28). VoxNet: A 3d Convolutional Neural Network for Real-time Object Recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_23","unstructured":"Engelcke, M., Rao, D., Wang, D.Z., Tong, C.H., and Posner, I. (June, January 29). Vote3Deep: Fast Object Detection in 3d Point Clouds Using Efficient Convolutional Neural Networks. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Song, W., Tian, Y., Ji, S., Sung, Y., Wen, L., Zhang, T., Song, L., and Gozho, A. (2020). VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification. Appl. Sci., 10.","DOI":"10.3390\/app10196735"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.18280\/ts.370614","article-title":"Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering","volume":"37","author":"Saglam","year":"2020","journal-title":"Traitement du Signal"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cad.2019.02.006","article-title":"Data-driven Upsampling of Point Clouds","volume":"112","author":"Zhang","year":"2019","journal-title":"Compuer-Aided Des."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5081","DOI":"10.1007\/s11042-018-6838-z","article-title":"Parameter optimization criteria guided 3D point cloud classification","volume":"78","author":"Li","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neucom.2018.09.008","article-title":"DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model","volume":"321","author":"Wu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.neucom.2020.06.095","article-title":"Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds","volume":"413","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1007\/s10489-020-02004-8","article-title":"PointFusionNet: Point feature fusion network for 3D point clouds analysis","volume":"51","author":"Liang","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, C., Zanotti Fragonara, L., and Tsourdos, A. (2020). Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions. Appl. Sci., 10.","DOI":"10.3390\/app10072391"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3588","DOI":"10.1109\/TGRS.2019.2958517","article-title":"TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201310). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_35","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_36","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the Inception Architecture for Computer Visio. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR, Las Vegas, NV, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). Richly-annotated 3d Reconstructions of Indoor Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., and Qiao, Y. (2018, January 8\u201314). Deep Learning on Point Sets with Parameterized Convolutional Filters. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Riegler, G., Osman Ulusoy, A., and Geiger, A. (2017, January 21\u201326). Octnet: Learning Deep 3d Representations at High Resolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.701"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Park, J., Koltun, V., and Zhou, Q.Y. (2018, January 18\u201321). Tangent Convolutions for Dense Prediction in 3d. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00409"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, Q., Wang, W., and Neumann, U. (2018, January 18\u201321). Recurrent slice networks for 3d segmentation of point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00278"},{"key":"ref_43","unstructured":"Zhang, Z., Hua, B.S., and Yeung, S.K. (November, January 27). ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics. Proceedings of the International Conference on Computer Vision(ICCV), Seoul, Korea."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:58:35Z","timestamp":1760137115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,22]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093209"],"URL":"https:\/\/doi.org\/10.3390\/s22093209","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,4,22]]}}}