Geomatics and Environmental Engineering, vol. 20, no. 4

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Vol. 20 No. 4 (2026): Geomatics and Environmental Engineering

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Submitted
Oct 13, 2025
Accepted
May 1, 2026
Published
May 30, 2026
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Point Cloud Technologies for Smart Cities: Acquisition, Processing, and Applications

https://doi.org/10.7494/geom.2026.20.4.123
Jagoda Hauzner
AGH University of Krakow, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, Department of Photogrammetry, Remote Sensing of Environment, and Spatial Engineering, Krakow, Poland
https://orcid.org/0009-0002-3221-8007

Corresponding Author(s) : Jagoda Hauzner

hauzner@agh.edu.pl

Geomatics and Environmental Engineering, Vol. 20 No. 4 (2026): Geomatics and Environmental Engineering

Abstract

Recent progress in LiDAR, UAV, and photogrammetric systems has made spatial data collection faster and more accessible. These tools enable the acquisition of detailed point clouds that form the foundation for many smart city applications. Efficient processing of these datasets is now a practical necessity, especially for everyday tasks such as monitoring roads and bridges, managing traffic, or building 3D city models used in digital twins. This paper reviews both classical and deep learning-based processing methods, data acquisition techniques, and multi-sensor integration strategies. Furthermore, the paper highlights applications beyond infrastructure, such as environmental monitoring of green areas and the analysis of pedestrian and bicycle networks. Despite the significant progress achieved in recent years, several open challenges remain. Among the most important are the need for standardized data formats, improved computational efficiency, and robust fusion of heterogeneous sensor data. Overcoming these difficulties is key to ensuring that digital twins and AI-based analysis become useful tools in practical urban management. Ultimately, continued progress in this field can make a meaningful contribution to the development of smarter and more sustainable cities.

Keywords

point clouds smart cities LiDAR machine learning digital twin
Hauzner, J. (2026). Point Cloud Technologies for Smart Cities: Acquisition, Processing, and Applications. Geomatics and Environmental Engineering, 20(4), 123–150. https://doi.org/10.7494/geom.2026.20.4.123
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References
  1. Liu B., Li Q., Zheng Z., Huang Y., Deng S., Huang Q., Liu W.: A review of multi-source data fusion and analysis algorithms in smart city construction: Facilitating real estate management and urban optimization. Algorithms, vol. 18(1), 2025, 30. https://doi.org/10.3390/a18010030.
  2. Huzzat A., Anpalagan A., Khwaja A.S., Woungang I., Alnoman A.A., Pillai A.S.: A comprehensive review of Digital Twin technologies in smart cities. Digital Engineering, vol. 4, 2025, 100040. https://doi.org/10.1016/j.dte.2025.100040.
  3. Mazzetto S.: A review of urban digital twins integration, challenges, and future directions in smart city development. Sustainability, vol. 16(19), 2024, 8337. https://doi.org/10.3390/su16198337.
  4. Nukpezah J.A., Apalowo J.T., Abutabenjeh S.: Why do cities go smart? Investigating the determinants of local engagement with smart cities technologies. Cities, vol. 163, 2025, 106036. https://doi.org/10.1016/j.cities.2025.106036.
  5. Alvi M., Dutta H., Minerva R., Crespi N., Raza S.M., Herath M.: Global perspectives on digital twin smart cities: Innovations, challenges, and pathways to a sustainable urban future. Sustainable Cities and Society, vol. 126, 2025, 106356. https://doi.org/10.1016/j.scs.2025.106356.
  6. Zhou Y., Tuzel O.: VoxelNet: End-to-end learning for point cloud based 3D object detection, [in:] 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition: 18–22 June 2018, Salt Lake City, Utah, IEEE, 2018, pp. 4490–4499. https://doi.org/10.1109/CVPR.2018.00472.
  7. Rusu R.B., Cousins S.: 3D is here: Point Cloud Library (PCL), [in:] 2011 IEEE International Conference on Robotics and Automation: 9–13 May, Shanghai, China, 2011, pp. 1–4. https://doi.org/10.1109/ICRA.2011.5980567.
  8. Lee J.J., Benes B.: RGB2Point: 3D point cloud generation from single RGB images, [in:] 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV): February 2025–06 March 2025, Tucson, AZ, USA, IEEE, 2025, pp. 2952–2962. https://doi.org/10.1109/WACV61041.2025.00292.
  9. Henry P., Krainin M., Herbst E., Ren X., Fox D.: RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments. The International Journal of Robotics Research, vol. 31(5), 2012, pp. 647–663. https://doi.org/10.1177/0278364911434148.
  10. Westoby M.J., Brasington J., Glasser N.F., Hambrey M.J., Reynolds J.M.: ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, vol. 179, 2012, pp. 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021.
  11. Maksymova I., Steger C., Druml N.: Review of LiDAR sensor data acquisition and compression for automotive applications. Proceedings, vol. 2(13), 2018, 852. https://doi.org/10.3390/proceedings2130852.
  12. Uciechowska-Grakowicz A., Herrera-Granados O., Biernat S., Bac-Bronowicz J.: Usage of airborne LiDAR data and high-resolution remote sensing images in implementing the smart city concept. Remote Sensing, vol. 15(24), 2023, 5776. https://doi.org/10.3390/rs15245776.
  13. Prendinger H., Gajananan K., Bayoumy Zaki A., Fares A., Molenaar R., Urbano D., van Lint H., Gomaa W.: Tokyo Virtual Living Lab: Designing smart cities based on the 3D internet. IEEE Internet Computing, vol. 17(6), 2013, pp. 30–38. https://doi.org/10.1109/MIC.2013.87.
  14. Mahdianpari M., Granger J.E., Mohammadimanesh F., Warren S., Puestow T., Salehi B., Brisco B.: Smart solutions for smart cities: Urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John’s, NL, Canada. Journal of Environmental Management, vol. 280, 2021, 111676. https://doi.org/10.1016/j.jenvman.2020.111676.
  15. Chen J., Zhao Y., Meng C., Liu Y.: Multi-feature aggregation for semantic segmentation of an urban scene point cloud. Remote Sensing, vol. 14(20), 2022, 5134. https://doi.org/10.3390/rs14205134.
  16. Wu S.-C., Tateno K., Navab N., Tombari F.: SCFusion: Real-time incremental scene reconstruction with semantic completion, [in:] 2020 International Conference on 3D Vision: 3DV 2020: Proceedings: 25–28 November 2020, Virtual Event, IEEE, 2020, pp. 801–810. https://doi.org/10.1109/3DV50981.2020.00090.
  17. Lang A.H., Vora S., Caesar H., Zhou L., Yang J., Beijbom O.: PointPillars: Fast encoders for object detection from point clouds, [in:] 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019): 15–20 June 2019, Long Beach, California, USA, IEEE, 2019, pp. 12689–12697. https://doi.org/10.1109/CVPR.2019.01298.
  18. Qi C.R., Litany O., He K., Guibas L.: Deep Hough voting for 3D object detection in point clouds, [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 9276–9285. https://doi.org/10.1109/ICCV.2019.00937.
  19. Dai Z., Guan Z., Chen Q., Xu Y., Sun F.: Enhanced object detection in autonomous vehicles through LiDAR – camera sensor fusion. World Electric Vehicle Journal, vol. 15(7), 2024, 297. https://doi.org/10.3390/wevj15070297.
  20. Ni P., Li X., Xu W., Zhou X., Jiang T., Hu W.: Robust 3D semantic segmentation method based on multi-modal collaborative learning. Remote Sensing, vol. 16(3), 2024, 453. https://doi.org/10.3390/rs16030453.
  21. Charles R.Q., Su H., Kaichun M., Guibas L.J.: PointNet: Deep learning on point sets for 3D classification and segmentation, [in:] 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017): 21–26 July 2017, Honolulu, Hawaii, USA, IEEE, 2017, pp. 77–85. https://doi.org/10.1109/CVPR.2017.16.
  22. Thomas H., Qi C.R., Deschaud J.-E., Marcotegui B., Goulette F., Guibas L.: KPConv: Flexible and deformable convolution for point clouds, [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 6410–6419. https://doi.org/10.1109/ICCV.2019.00651.
  23. Hu Q., Yang B., Xie L., Rosa S., Guo Y., Wang Z., Trigoni N., Markham A.: RandLA-Net: Efficient semantic segmentation of large-scale point clouds. 2020. https://doi.org/10.48550/arXiv.1911.11236.
  24. Wang L., Huang Y., Hou Y., Zhang S., Shan J.: Graph attention convolution for point cloud semantic segmentation, [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 10288–10297. https://doi.org/10.1109/CVPR.2019.01054.
  25. Xu X., Dong S., Xu T., Ding L., Wang J., Jiang P., Song L., Li J.: FusionRCNN: LiDAR-camera fusion for two-stage 3D object detection. Remote Sensing, vol. 15(7), 2023, 1839. https://doi.org/10.3390/rs15071839.
  26. Roriz R., Silva H., Dias F., Gomes T.: A survey on data compression techniques for automotive LiDAR point clouds. Sensors, vol. 24(10), 2024, 3185. https://doi.org/10.3390/s24103185.
  27. Li Y., An J., He N., Li Y., Han Z., Chen Z., Qu Y.: A review of simultaneous localization and mapping algorithms based on Lidar. World Electric Vehicle Journal, vol. 16(2), 2025, 56. https://doi.org/10.3390/wevj16020056.
  28. Liu S., Wang T., Zhang Y., Zhou R., Dai C., Zhang Y., Lei H., Wang H.: Rethinking of learning-based 3D keypoints detection for large-scale point clouds registration. International Journal of Applied Earth Observation and Geoinformation, vol. 112, 2022, 102944. https://doi.org/10.1016/j.jag.2022.102944.
  29. Besl P.J., McKay N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14(2), 1992, pp. 239–256. https://doi.org/10.1109/34.121791.
  30. Betsas T., Georgopoulos A., Doulamis A., Grussenmeyer P.: Deep learning on 3D semantic segmentation: A detailed review. Remote Sensing, vol. 17(2), 2025, 298. https://doi.org/10.3390/rs17020298.
  31. Balawejder M., Warchoł A., Konttinen K.: Energy efficiency in agricultural production – Experience from land consolidation in Poland and Finland. Energies, vol. 16(22), 2023, 7598. https://doi.org/10.3390/en16227598.
  32. Tamimi R., Toth Ch.: Accuracy assessment of UAV Lidar compared to traditional total station for geospatial data collection in land surveying contexts. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-2–2024, 2024, pp. 421–426. https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-421-2024.
  33. Puente I., Gonzales-Jorge H., Riveiro B., Arias P.: Accuracy verification of the Lynx Mobile Mapper system. Optics & Laser Technology, vol. 45, 2013, pp. 578–586. https://doi.org/10.1016/j.optlastec.2012.05.029.
  34. Hu Ch., Peng D., LV f., Sun H., Zhao T., Li W.: Application of terrestrial laser scanner in engineering survey. IOP Conference Series: Earth and Environmental Science, vol. 783(1), 2021, 012084. https://doi.org/10.1088/1755-1315/783/1/012084.
  35. Algadhi A., Psimoulis P., Grizi A., Neves L.: Assessment of the accuracy of terrestrial laser scanners in detecting local surface anomaly. Remote Sensing, vol. 16(24), 2024, 4647. https://doi.org/10.3390/rs16244647.
  36. Zou Y., Weinacker H., Koch B.: Towards urban scene semantic segmentation with deep learning from LiDAR point clouds: A case study in Baden-Württemberg, Germany. Remote Sensing, vol. 13(16), 2021, 3220. https://doi.org/10.3390/rs13163220.
  37. Xie X., Wei H., Yang Y.: Real-time LiDAR point-cloud moving object segmentation for autonomous driving. Sensors, vol. 23(1), 2023, 547. https://doi.org/10.3390/s23010547.
  38. Su H., Maji S., Kalogerakis E., Learned-Miller E.: Multi-view convolutional neural networks for 3D shape recognition, [in:] 2015 IEEE International Conference on Computer Vision (ICCVW 2015): Proceedings: 7–13 December 2015, Santiago, Chile, IEEE, 2015, pp. 945–953. https://doi.org/10.1109/ICCV.2015.114.
  39. Cadena C., Carlone L., Carrillo H., Latif Y., Scaramuzza D., Neira J., Reid I., Leonard J.J.: Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, vol. 32(6), 2016, pp. 1309–1332. https://doi.org/10.1109/TRO.2016.2624754.
  40. Song S., Lichtenberg S.P., Xiao J.: SUN RGB-D: A RGB-D scene understanding benchmark suite, [in:] 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015): 7–12 June 2015, Boston, Massachusetts, USA, IEEE, 2015, pp. 567–576. https://doi.org/10.1109/CVPR.2015.7298655.
  41. Newcombe R.A., Fitzgibbon A., Izadi S., Hilliges O., Molyneaux D., Kim D., Davison A.J., Kohi P., Shotton J., Hodges S.: KinectFusion: Real-time dense surface mapping and tracking, [in:] 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2011): 26–29 October 2011, Basel, Switzerland, IEEE, 2011, pp. 127–136. https://doi.org/10.1109/ISMAR.2011.6092378.
  42. Hermans A., Floros G., Leibe B.: Dense 3D semantic mapping of indoor scenes from RGB-D images, [in:] 2014 IEEE International Conference on Robotics and Automation (ICRA 2014): 31 May – 7 June 2014, Hong Kong, China, IEEE, 2014, pp. 2631–2638. https://doi.org/10.1109/ICRA.2014.6907236.
  43. Gao H., Kong G., Huang X., Fan H., Zhong R.: RobotSLAM: A lightweight low-cost 3D LiDAR SLAM handheld device. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-G-2025, 2025, pp. 497–503. https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-497-2025.
  44. Zhang J., Singh S.: LOAM: Lidar odometry and mapping in real-time, [in:] Robotics: Science and Systems X, 2014. https://doi.org/10.15607/RSS.2014.X.007.
  45. Engel J., Schöps T., Cremers D.: LSD-SLAM: Large-scale direct monocular SLAM, [in:] Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds.), Computer Vision – ECCV 2014, Lecture Notes in Computer Science, vol. 8690, Springer, Cham 2014, pp. 834–849. https://doi.org/10.1007/978-3-319-10605-2_54.
  46. Montiel-Marín S., Llamazares Á., Antunes M., Revenga P.A., Bergasa L.M.: Point cloud painting for 3D object detection with camera and automotive 3+1D RADAR fusion. Sensors, vol. 24(4), 2024, 1244. https://doi.org/10.3390/s24041244.
  47. Varior R.R., Shuai B., Lu J., Xu D., Wang G.: A Siamese long short-term memory architecture for human re-identification, [in:] Leibe B., Matas J., Sebe N., Welling M. (eds.), Computer Vision – ECCV 2016, Lecture Notes in Computer Science, vol. 9911, Springer, Cham 2016, pp. 135–153. https://doi.org/10.1007/978-3-319-46478-7_9.
  48. Nebiker S., Meyer J., Blaser S., Ammann M., Rhyner S.: Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras: The use case of on-street parking statistics. Remote Sensing, vol. 13(16), 2021, 3099. https://doi.org/10.3390/rs13163099.
  49. Pargieła K.: Optimising UAV data acquisition and processing for photogrammetry: A review. Geomatics and Environmental Engineering, vol. 17(3), 2023, pp. 29–59. https://doi.org/10.7494/geom.2023.17.3.29.
  50. Zhuang L., Zhong X., Xu L., Tian C., Yu W.: Visual SLAM for unmanned aerial vehicles: Localization and perception. Sensors, vol. 24(10), 2024, 2980. https://doi.org/10.3390/s24102980.
  51. Wang D., Li W., Liu X., Li N., Zhang C.: UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution. Computers and Electronics in Agriculture, vol. 175, 2020, 105523. https://doi.org/10.1016/j.compag.2020.105523.
  52. Li D., Lu C., Chen Z., Guan J., Zhao J., Du J.: Graph neural networks in point clouds: A survey. Remote Sensing, vol. 16(14), 2024, 2518. https://doi.org/10.3390/rs16142518.
  53. Wang Y., Sun Y., Liu Z., Sarma S.E., Bronstein M.M., Solomon J.M.: Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, vol. 38(5), 2019, pp. 1–12. https://doi.org/10.1145/3326362.
  54. Wu Z., Song S., Khosla A., Yu F., Zhang L., Tang X., Xiao J.: 3D ShapeNets: A deep representation for volumetric shapes, [in:] 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015): 7–12 June 2015, Boston, Massachusetts, USA, IEEE, 2015, pp. 1912–1920. https://doi.org/10.1109/CVPR.2015.7298801.
  55. Gonzalez R.C., Woods R.E.: Digital Image Processing (4th ed.). Pearson, New York 2018.
  56. Zhang Z., Dai Y., Sun J.: Deep learning based point cloud registration: An overview. Virtual Reality & Intelligent Hardware, vol. 2(3), 2020, pp. 222–246. https://doi.org/10.1016/j.vrih.2020.05.002.
  57. Choy C., Park J., Koltun V.: Fully convolutional geometric features, [in:] 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019): 15–20 June 2019, Long Beach, California, USA, IEEE, 2019, pp. 8957–8965. https://doi.org/10.1109/ICCV.2019.00905.
  58. Wang Y., Solomon J. M.: Deep closest point: Learning representations for point cloud registration, [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 3523–3532. pp. 3522–3531. https://doi.org/10.1109/ICCV.2019.00362.
  59. Gojcic, Z., Zhou, C., Wegner, J. D. and Wieser, A.: The perfect match: 3D point cloud matching with smoothed densities, [in:] 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019): 15–20 June 2019, Long Beach, California, USA, IEEE, 2019, pp. 5540–5549. https://doi.org/10.1109/CVPR.2019.00569.
  60. Maturana D., Scherer S.: VoxNet: A 3D convolutional neural network for real-time object recognition, [in:] 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015): 28 September – 2 October 2015, Hamburg, Germany, IEEE, 2015, pp. 922–928. https://doi.org/10.1109/IROS.2015.7353481.
  61. Yan X., Zheng C., Li Z., Wang S., Cui S.: PointASNL: Robust point clouds processing using nonlocal neural networks with adaptive sampling, [in:] 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020): 14–19 June 2020, Seattle, Washington, USA, IEEE, 2020, pp. 5588–5597. https://doi.org/10.1109/CVPR42600.2020.00563.
  62. Gao X., Yang R., Tan J., Liu Y.: Floor plan reconstruction from indoor 3D point clouds using iterative RANSAC line segmentation. Journal of Building Engineering, vol. 89, 2024, 109238. https://doi.org/10.1016/j.jobe.2024.109238.
  63. Xu M., Ding R., Zhao H., Qi X.: PAConv: Position adaptive convolution with dynamic kernel assembling on point clouds, [in:] 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021): 19–25 June 2021, Virtual Conference, IEEE, 2021, pp. 3172–3181. https://doi.org/10.1109/CVPR46437.2021.00319.
  64. Engelmann F., Bokeloh M., Fathi A., Leibe B., NieBner M.: 3D-MPA: Multi-proposal aggregation for 3D semantic instance segmentation, [in:] 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020): 14–19 June 2020, Seattle, Washington, USA, IEEE, 2020, pp. 9028–9037. https://doi.org/10.1109/CVPR42600.2020.00905.
  65. Chen S., Fang J., Zhang Q., Liu W., Wang X.: Hierarchical aggregation for 3D instance segmentation, [in:] 2021 IEEE/CVF International Conference on Computer Vision (ICCV 2021): 11–17 October 2021, Virtual Conference, IEEE, 2021, pp. 15447–15456. https://doi.org/10.1109/ICCV48922.2021.01518.
  66. Behley J., Garbade M., Milioto A., Quenzel J., Behnke S., Stachniss C., Gall J.: [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 9296–9306. https://doi.org/10.1109/ICCV.2019.00939.
  67. Dai A., Chang A.X., Savva M., Halber M., Funkhouser T., Niessner M.: ScanNet: Richly-annotated 3D reconstructions of indoor scenes, [in:] 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017): 21–26 July 2017, Honolulu, Hawaii, USA, IEEE, 2017, pp. 2432–2443. https://doi.org/10.1109/CVPR.2017.261.
  68. Sheffer A., Polthier K. (eds.): SGP 2006: Proceedings of the Fourth Eurographics Symposium on Geometry Processing. Eurographics Association, Aire-la Ville 2006.
  69. Zhang H., Wang C., Tian S., Lu B., Zhang L., Ning X., Bai X.: Deep learningbased 3D point cloud classification: A systematic survey and outlook. Displays, vol. 79, 2023, 102456. https://doi.org/10.1016/j.displa.2023.102456.
  70. Kazhdan M., Bolitho M., Hoppe H.: Poisson Surface Reconstruction, [in:] Sheffer A., Polthier K. (eds.), SGP 2006: Proceedings of the Fourth Eurographics Symposium on Geometry Processing, Eurographics Association, Aire-la-Ville 2006, pp. 61–70. https://doi.org/10.2312/SGP/SGP06/061-070.
  71. Zhao Y., Fang G., Guo Y., Guibas L., Tombari F., Birdal T.: 3DPointCaps++: Learning 3D representations with capsule networks. International Journal of Computer Vision, vol. 130(9), 2022, pp. 2321–2336. https://doi.org/10.1007/s11263-022-01632-6.
  72. Choy C.B., Xu D., Gwak J., Chen K., Savarese S.: 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction, [in:] Leibe B., Matas J., Sebe N., Welling M. (eds.), Computer Vision – ECCV 2016, Lecture Notes in Computer Science, vol. 9912, Springer, Cham 2016, pp. 628–644. https://doi.org/10.1007/978-3-319-46484-8_38.
  73. Weinmann M.: Reconstruction and Analysis of 3D Scenes. Springer, Cham 2016. https://doi.org/10.1007/978-3-319-29246-5.
  74. Župan R., Vinković A., Nikçi R., Pinjatela B.: Automatic 3D building model generation from airborne LiDAR data and OpenStreetMap using procedural modeling. Information, vol. 14(7), 2023, 394. https://doi.org/10.3390/info14070394.
  75. Garnett R., Adams M.: LiDAR – A technology to assist with smart cities and climate change resilience: A case study in an urban metropolis. ISPRS International Journal of Geo-Information, vol. 7(5), 2018, 161. https://doi.org/10.3390/ijgi7050161.
  76. Lim T.K., Rajabifard A., Khoo V., Sabri S., Chen Y.: The smart city in Singapore: How environmental and geospatial innovation lead to urban livability and environmental sustainability, [in:] Kim H.M., Sabri S., Kent A. (eds.), Smart Cities for Technological and Social Innovation: Case Studies, Current Trends, and Future Steps, Elsevier, Academic Press 2021, pp. 29–49. https://doi.org/10.1016/B978-0-12-818886-6.00003-4.
  77. Sacoto-Cabrera E.J., Perez-Torres A., Tello-Oquendo L., Cerrada M.: IoT, AI, and digital twins in smart cities: A systematic review for a thematic mapping and research agenda. Smart Cities, vol. 8(5), 2025, 175. https://doi.org/10.3390/smartcities8050175.
  78. Lai J., Chen X., Dong J., Zhu J., Guo J., Wang Z., Liang X.: Intelligent geometric deformation analysis method of railway bridges driven by digital twin. Intelligent Transportation Infrastructure, vol. 4, 2025, liaf030. https://doi.org/10.1093/iti/liaf030.
  79. Sakurada K., Okatani T., Deguchi K.: Detecting changes in 3D structure of a scene from multi-view images captured by a vehicle-mounted camera, [in:] 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013): 23–28 June 2013, Portland, Oregon, USA, IEEE, 2013, pp. 137–144. https://doi.org/10.1109/CVPR.2013.25.
  80. Valada A., Mohan R., Burgard W.: Self-supervised model adaptation for multimodal semantic segmentation. International Journal of Computer Vision, vol. 128(5), 2020, pp. 1239–1285. https://doi.org/10.1007/s11263-019-01188-y.
  81. Torres P., Marques H., Marques P.: Pedestrian detection with LiDAR technology in smart-city deployments – challenges and recommendations. Computers, vol. 12(3), 2023, 65. https://doi.org/10.3390/computers12030065.
  82. Sun P., Kretzschmar H., Dotiwalla X., Chouard A., Patnaik V., Tsiu P., Guo J., Zhou Y., Chai Y., Caine B., Vasudevan V., Han W., Ngiam J., Zhao H., Timofeev A., Ettinger S., Krivokon M., Gao A., Joshi A., ..., Anguelov D.: Scalability in perception for autonomous driving: Waymo Open Dataset, [in:] 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020): 14–19 June 2020, Seattle, Washington, USA, IEEE, 2020, pp. 2446–2454. https://doi.org/10.1109/CVPR42600.2020.00252.
  83. Balado J., Díaz-Vilariño L., Arias P., Lorenzo H.: Point clouds for direct pedestrian pathfinding in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 148, 2019, pp. 184–196. https://doi.org/10.1016/j.isprsjprs.2019.01.004.
  84. Jahromi M.N., Samany N.N., Argany M., Mostafavi M.A.: Enhancing sidewalk accessibility assessment for wheelchair users: An adaptive weighting fuzzy-based approach. Heliyon, vol. 11(1), 2025, e41101. https://doi.org/10.1016/j.heliyon.2024.e41101.
  85. Mi X., Yang B., Dong Z., Chen C., Gu J.: Automated 3D road boundary extraction and vectorization using MLS point clouds. IEEE Transactions on Intelligent Transportation Systems, vol. 23(6), 2022, pp. 5287–5297. https://doi.org/10.1109/TITS.2021.3052882.
  86. Wang Y., Fang H.: Estimation of LAI with the LiDAR technology: A review. Remote Sensing, vol. 12(20), 2020, 3457. https://doi.org/10.3390/rs12203457.
  87. Szostak M., Pająk M.: LiDAR point clouds usage for mapping the vegetation cover of the “Fryderyk” mine repository. Remote Sensing, vol. 15(1), 2022, 201. https://doi.org/10.3390/rs15010201.
  88. Wężyk P., Szostak M., Krzaklewski W., Pająk M., Pierzchalski M., Szwed P., Hawryło P., Ratajczak M.: Landscape monitoring of post-industrial areas using LiDAR and GIS technology. Geodesy and Cartography, vol. 64(1), 2015, pp. 125–137. https://doi.org/10.1515/geocart-2015-0010.
  89. Tompalski P., Wężyk P.: LiDAR and VHRS data for assessing living quality in cities – an approach based on 3D spatial indices. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIX-B6, 2012, pp. 173–176. https://doi.org/10.5194/isprsarchives-XXXIX-B6-173-2012.
  90. Yang G., Huang X., Hao Z., Liu M.-Y., Belongie S., Hariharan B.: Point-Flow: 3D point cloud generation with continuous normalizing flows, [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 4540–4549. https://doi.org/10.1109/ICCV.2019.00464.
  91. Fatima Z., Zardari S., Tanveer M.H.: Advancing industrial object detection through domain adaptation: A solution for Industry 5.0. Actuators, vol. 13(12), 2024, 513. https://doi.org/10.3390/act13120513.
  92. Hossain M.Z., Sohel F., Shiratuddin M.F., Laga H.: A comprehensive survey of deep learning for image captioning. ACM Computing Surveys, vol. 51(6), 2019, 118. https://doi.org/10.1145/3295748.
  93. Zhang S., Li Y., Zhang S., Shahabi F., Xia S., Deng Y., Alshurafa N.: Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors, vol. 22(4), 2022, 1476. https://doi.org/10.3390/s22041476.
  94. Tang H., Liu Z., Zhao S., Lin Y., Lin J., Wang H., Han S.: Searching efficient 3D architectures with sparse point-voxel convolution, [in:] Vedaldi A., Bischof H., Brox T., Frahm J.-M. (eds.), Computer Vision – ECCV 2020, Springer, Cham 2020, pp. 685–702. https://doi.org/10.1007/978-3-030-58604-1_41.
  95. Liu Z., Zhou S., Suo C., Yin P., Chen W., Wang H., Li H., Liu Y.: LPD-Net: 3D point cloud learning for large-scale place recognition and environment analysis, [in:] 2019 IEEE/CVF International Conference on Computer Vision (ICCV 2019): 27 October – 2 November 2019, Seoul, South Korea, IEEE, 2019, pp. 2831–2840. https://doi.org/10.1109/ICCV.2019.00292.
  96. Liang P., Zhou G.Q., Lu Y.L., Zhou X., Song B.: Hole-filling algorithm for airborne LiDAR point cloud data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3/W10, 2020, pp. 739–745. https://doi.org/10.5194/isprs-archives-XLII-3-W10-739-2020.
  97. Chen C., Li Y., Zhao N., Guo J., Liu G., the PLoS One Editors: Correction: A fast and robust interpolation filter for airborne LiDAR point clouds. PLoS One, vol. 15(5), 2020, e0233128. https://doi.org/10.1371/journal.pone.0233128.
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