{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T01:34:55Z","timestamp":1771810495150,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The registration of panoramic images and mobile light detection and ranging (LiDAR) data is quite challenging because different imaging mechanisms and viewing angle differences generate significant geometric and radiation distortions between the two multimodal data sources. To address this problem, we propose a registration method for panoramic images and mobile LiDAR data based on the hybrid geometric structure index feature of phase. We use the initial GPS\/IMU to transform the mobile LiDAR data into an intensity map and align the two images to complete registration. Firstly, a novel feature descriptor called a hybrid geometric structure index of phase (HGIFP) is built to capture the structural information of the images. Then, a set of corresponding feature points is obtained from the two images using the constructed feature descriptor combined with a robust false-match elimination algorithm. The average pixel distance of the corresponding feature points is used as the error function. Finally, in order to complete the accurate registration of the mobile LiDAR data and panoramic images and improve computational efficiency, we propose the assumption of local motion invariance of 3D\u20132D corresponding feature points and minimize the error function through multiple reprojections to achieve the best registration parameters. The experimental results show that the method in this paper can complete the registration of panoramic images and the mobile LiDAR data under a rotation error within 12\u00b0 and a translation error within 2 m. After registration, the average error of rotation is about 0.15\u00b0, and the average error of translation is about 1.27 cm. Moreover, it achieves a registration accuracy of less than 3 pixels in all cases, which outperforms the current five state-of-the-art methods, demonstrating its superior registration performance.<\/jats:p>","DOI":"10.3390\/rs14194783","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T03:34:17Z","timestamp":1664163257000},"page":"4783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Registration for Panoramic Images and Mobile LiDAR Data Based on Phase Hybrid Geometry Index Features"],"prefix":"10.3390","volume":"14","author":[{"given":"Genyi","family":"Wan","sequence":"first","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Ningning","family":"Zhu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Ruizhuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Ruofei","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s11263-007-0081-9","article-title":"3D urban scene modeling integrating recognition and reconstruction","volume":"78","author":"Cornelis","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s12518-013-0105-9","article-title":"Co-registration of aerial photogrammetric and LiDAR point clouds in urban environments using automatic plane correspondence","volume":"5","author":"Armenakis","year":"2013","journal-title":"Appl. Geomat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1109\/JSTARS.2020.3033770","article-title":"A phase-congruency-based scene abstraction approach for 2d-3d registration of aerial optical and LiDAR images","volume":"14","author":"Megahed","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hou, M., Li, S.K., Jiang, L., Wu, Y., Hu, Y., Yang, S., and Zhang, X. (2016). A new method of gold foil damage detection in stone carving relics based on multi-temporal 3D LiDAR point clouds. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5050060"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Feraco, S., Bonfitto, A., Amati, N., and Tonoli, A. (2020, January 16\u201319). A LIDAR-Based Clustering Technique for Obstacles and Lane Boundaries Detection in Assisted and Autonomous Driving. Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, St. Louis, MO, USA.","DOI":"10.1115\/1.0002078V"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Takeuchi, E., Yoshihara, Y., and Yoshiki, N. (2015, January 15\u201318). Blind Area Traffic Prediction Using High Definition Maps and LiDAR for Safe Driving Assist. Proceedings of the IEEE Conference on Intelligent Transportation Systems (ITSC), Las Palmas, Spain.","DOI":"10.1109\/ITSC.2015.373"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2019.02.019","article-title":"Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network","volume":"151","author":"Huang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.isprsjprs.2018.04.024","article-title":"Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud","volume":"143","author":"Wu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ouyang, Z., Liu, Y., Zhang, C., and Niu, J. (2017, January 12\u201315). A cgans-based scene reconstruction model using lidar point cloud. Proceedings of the 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA\/IUCC), Guangzhou, China.","DOI":"10.1109\/ISPA\/IUCC.2017.00167"},{"key":"ref_10","unstructured":"Boehm, J., and Becker, S. (2007, January 9\u201312). Automatic Marker-free Registration of Terrestrial Laser Scans using Reflectance Features. Proceedings of the 8th Conference Optical 3-D Measurement Techniques, Zurich, Switzerland."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2013.04.002","article-title":"A shape-based segmentation method for mobile laser scanning point clouds","volume":"81","author":"Yang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","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 Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Schlosser, J., Chow, C.K., and Kira, Z. (2016, January 16\u201321). Fusing LIDAR and images for pedestrian detection using convolutional neural networks. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487370"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Premebida, C., Carreira, J., Batista, J., and Nunes, U. (2014, January 14). Pedestrian Detection Combining RGB and Dense LIDAR Data. Proceedings of the International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943141"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.earscirev.2017.04.007","article-title":"Review of earth science research using terrestrial laser scanning","volume":"169","author":"Telling","year":"2017","journal-title":"Earth Sci. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2017.12.005","article-title":"Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features","volume":"136","author":"Li","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peng, S., Ma, H., and Zhang, L. (2019). Automatic Registration of Optical Images with Airborne LiDAR Point Cloud in Urban Scenes Based on Line-Point Similarity Invariant and Extended Collinearity Equations. Sensors, 19.","DOI":"10.3390\/s19051086"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2021.09.010","article-title":"Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features","volume":"181","author":"Zhu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2013.11.015","article-title":"Automatic registration of optical imagery with 3D LIDAR data using statistical similarity","volume":"88","author":"Parmehr","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, W., Gong, Y., Yang, M., and Liu, T. (2021, January 5\u20137). Point Cloud Depth Map and Optical Image Registration Based on Improved RIFT Algorithm. Proceedings of the 2021 IEEE 13th International Conference on Computer Research and Development (ICCRD), Beijing, China.","DOI":"10.1109\/ICCRD51685.2021.9386501"},{"key":"ref_21","unstructured":"Taylor, Z., and Nieto, J. (2013, January 6\u201310). Automatic calibration of lidar and camera images using normalized mutual information. Proceedings of the 2013 IEEE Conference on Robotics and Automation (ICRA 2013), Karlsruhe, Germany."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, R., Ferrie, F.P., and Macfarlane, J. (2012, January 18\u201320). Automatic registration of mobile lidar and spherical panoramas. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238912"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2014.01.009","article-title":"A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences","volume":"90","author":"Ye","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shao, J., Zhang, W., Zhu, Y., and Shen, A. (2017, January 18\u201322). Fast registration of terrestrial LiDAR point cloud and sequence images. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u2014ISPRS Archives, Wuhan, China.","DOI":"10.5194\/isprs-archives-XLII-2-W7-875-2017"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, N., Jia, Y., and Ji, S. (2018). Registration of Panoramic\/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features. Sensors, 18.","DOI":"10.3390\/s18051651"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cui, T., Ji, S., Shan, J., Gong, J., and Liu, K. (2017). Line-based registration of panoramic images and LiDAR point clouds for mobile mapping. Sensors, 17.","DOI":"10.20944\/preprints201612.0016.v1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"829","DOI":"10.14358\/PERS.85.11.829","article-title":"Semiautomatically register MMS LiDAR points and panoramic image sequence using road lamp and lane","volume":"85","author":"Zhu","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kaminsky, R.S., Snavely, N., Seitz, S.T., and Szeliski, R. (2009, January 20\u201325). Alignment of 3D Point Clouds to Overhead Images. Proceedings of the Second IEEE Workshop on Internet Vision, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5204180"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"731","DOI":"10.14358\/PERS.79.8.731","article-title":"Registration of optical images with LiDAR data and its accuracy assessment","volume":"79","author":"Zheng","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TPAMI.2005.152","article-title":"Alignment of continuous video onto 3D point clouds","volume":"27","author":"Zhao","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2015.05.006","article-title":"Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models","volume":"106","author":"Abayowa","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_32","unstructured":"Zhao, Y., Wang, Y., and Tsai, Y. (2016, January 16\u201321). 2D-image to 3D-range registration in urban environments via scene categorisation and combination of similarity measurements. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Taylor, Z., Nieto, J., and Johnson, D. (2013, January 3\u20137). Automatic calibration of multimodal sensor systems using a gradient orientation measure. Proceedings of the IEEE International Conference on Intelligent Robots & Systems (IROS), Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696516"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.isprsjprs.2014.12.025","article-title":"Automatic registration of UAV-borne sequent images and LiDAR data","volume":"101","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/rob.20204","article-title":"Scan registration for autonomous mining vehicles using 3D-NDT","volume":"24","author":"Magnusson","year":"2007","journal-title":"J. Field Robot."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for Registration of 3-D Shapes. IEEE T rans","volume":"14","author":"Besl","year":"1992","journal-title":"Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2007.05.012","article-title":"A method for automated registration of unorganised point clouds","volume":"63","author":"Bae","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.isprsjprs.2013.02.019","article-title":"Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge","volume":"79","author":"Gressin","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2019.10.009","article-title":"NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement","volume":"158","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1109\/TIP.2019.2959244","article-title":"RIFT: Multi-modal image matching based on radiation-variation insensitive feature transform","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","unstructured":"Horn, B., Klaus, B., and Horn, P. (1986). Robot Vision, MIT Press."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1109\/83.661190","article-title":"Efficient and reliable schemes for nonlinear diffusion filtering","volume":"7","author":"Weickert","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1007\/s11263-018-1117-z","article-title":"Locality preserving matching","volume":"127","author":"Ma","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_44","first-page":"1727","article-title":"Heterologous Images Matching Considering Anisotropic Weighted Moment and Absolute Phase Orientation","volume":"46","author":"Yao","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4783\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:55Z","timestamp":1760143135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,24]]},"references-count":44,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194783"],"URL":"https:\/\/doi.org\/10.3390\/rs14194783","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,24]]}}}