Research using HELIOS

  1. Arnal-Anger, L., Lejemble, T., Coeurjolly, D., Barthe, L., & Mellado, N. (2026). Survey on differential estimators for 3d point clouds. Computer Graphics Forum, e70394. doi:10.1111/cgf.70394.

  2. Devereux, T., Lowe, T., Rivory, J., Bohn Reckziegel, R., Calders, K., Aryal, R. R., Eaton, G., Cooper, Z., Levick, S., Phinn, S., & Woodgate, W. (2026). RayExtract: A fast, scalable method for tree volume reconstruction from terrestrial laser scanning. Remote Sensing of Environment, 334, 115162. doi:10.1016/j.rse.2025.115162.

  3. Florakis, K., Letort, V., Canals, R., Faÿ, G., & Trevezas, S. (2026). Adaptive local maxima windows for tree detection: A point process perspective. Spatial Statistics, 72, 100954. doi:10.1016/j.spasta.2026.100954.

  4. Huang, S., Wang, R., & Wang, X. (2026). BuildingWorld: A Structured 3D Building Dataset for Urban Foundation Models. Proceedings of the AAAI Conference on Artificial Intelligence, 40(7), 5085–5094. doi:10.1609/aaai.v40i7.37422.

  5. Jia, S., De Vugt, L., Mayr, A., Anders, K., Liu, C., & Rutzinger, M. (2026). Change tensor: Estimating complex topographic changes from point clouds using Riemann manifold surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 232, 766–786. doi:10.1016/j.isprsjprs.2026.01.009.

  6. Jiang, J., Shen, Y., Wang, J., Kissling, W. D., Hollaus, M., Su, H., Wang, J., Ferreira, V., & Pfeifer, N. (2026). Cross-platform forest understanding: A multi-platform synergistic training framework for generalized forest point cloud segmentation. Remote Sensing of Environment, 342, 115467. doi:10.1016/j.rse.2026.115467.

  7. Kempf, D., Weiser, H., Kapitan, D., & Höfle, B. (2026). Teaming up as domain scientists and research software engineers for a sustainable HELIOS++ scientific software. In EGU General Assembly 2026, volume EGU26, 1–2. doi:10.5194/egusphere-egu26-10884.

  8. Liu, J., Wang, D., Gong, H., Wang, C., Zhu, J., & Wang, D. (2026). A synthetic data generation framework for deep learning-based LiDAR forest structure analysis. Remote Sensing of Environment, 341, 115436. doi:10.1016/j.rse.2026.115436.

  9. Pourdelan, H., Xiang, Z., Stewart, H., Nicholson, C., Tomko, M., & Khoshelham, K. (2026). Direct estimation of tree volume and aboveground biomass using deep regression with synthetic lidar data. arXiv preprint 2603.04683 [cs.LG]. arXiv:2603.04683.

  10. Stocker, O., Kouhi, R. M., Gahrouei, O. R., Badard, T., & Guilbert, E. (2026). QC-SF: Improving Computer Vision for Airborne LiDAR Point Clouds of Boreal Forests with Quebec Simulated Forest Dataset. In 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 5026–5036. IEEE, Tucson, AZ, USA. doi:10.1109/WACV61042.2026.00488.

  11. Tang, S., Chen, Y., Yu, T., Li, Y., Xie, L., Wang, W., & Guo, R. (2026). Optimized 3D Building Mapping and Reconstruction via Cross-View Collaboration in Densely Built-Up Areas. Photogrammetric Engineering & Remote Sensing, 92(1), 49–63. doi:10.14358/PERS.25-00100R3.

  12. Weiser, H., & Höfle, B. (2026). Neue Möglichkeiten im 3D-Umweltmonitoring durch virtuelles Laserscanning von dynamischen Szenen (VLS-4D). In 46. Wissenschaftlich-technische Jahrestagung der DGPF, 348–356. doi:https://doi.org/10.24407/KXP:1967891346.

  13. Weiser, H., & Höfle, B. (2026). Advancing vegetation monitoring with virtual laser scanning of dynamic scenes (VLS-4D): Opportunities, implementations and future perspectives. Methods in Ecology and Evolution, 17(1), 33–51. doi:10.1111/2041-210x.70189.

  14. Wysocki, O., Schwab, B., Biswanath, M. K., Greza, M., Zhang, Q., Zhu, J., Froech, T., Heeramaglore, M., Hijazi, I., Kanna, K., Pechinger, M., Chen, Z., Sun, Y., Segura, A. R., Xu, Z., AbdelGafar, O., Mehranfar, M., Yeshwanth, C., Liu, Y.-C., Yazdi, H., Wang, J., Auer, S., Anders, K., Bogenberger, K., Borrmann, A., Dai, A., Hoegner, L., Holst, C., Kolbe, T. H., Ludwig, F., Nießner, M., Petzold, F., Zhu, X. X., & Jutzi, B. (2026). TUM2TWIN: Introducing the large-scale multimodal urban digital twin benchmark dataset. ISPRS Journal of Photogrammetry and Remote Sensing, 232, 810–830. doi:10.1016/j.isprsjprs.2025.12.013.

  15. Yang, T., Zou, Y., Del Rey Castillo, E., Hou, L., & Zhong, J. (2026). Enhancing Scan-to-BIM for reinforced concrete bridges using point cloud completion techniques. Automation in Construction, 181, 106606. doi:10.1016/j.autcon.2025.106606.

  16. Zou, Y., Liang, T., Chen, W., Ren, Z., & Wen, Y. (2026). A BIM-Derived Synthetic Point Cloud (SPC) Dataset for Construction Scene Component Segmentation. Data, 11(1), 16. doi:10.3390/data11010016.

  17. Agrawal, A. K., Zou, Y., Chen, L., Abdelmegid, M., González, V. A., & Jin, H. (2025). Leveraging linked data for space constraints checking of mobile cranes in modular construction assembly lookahead planning. Advanced Engineering Informatics, 68, 103778. doi:10.1016/j.aei.2025.103778.

  18. Albert, W., Weiser, H., Tabernig, R., & Höfle, B. (2025). Wind during terrestrial laser scanning of trees: Simulation-based assessment of effects on point cloud features and leaf-wood classification. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-G-2025, 25–32. doi:10.5194/isprs-annals-X-G-2025-25-2025.

  19. Ali, M., Lohani, B., Hollaus, M., & Pfeifer, N. (2025). A hybrid approach for enhanced tree volume estimation of complex trees using terrestrial LiDAR. GIScience & Remote Sensing, 62(1), 2474836. doi:10.1080/15481603.2025.2474836.

  20. Hanousek, T., Novotný, J., Navrátilová, B., Švik, M., Krejza, J., & Janoutová, R. (2025). Complete workflow for detailed 3D forest reconstruction: from terrestrial laser scanning to complex 3D radiative transfer modelling. in silico Plants, 7(2), diaf019. doi:10.1093/insilicoplants/diaf019.

  21. Kuželka, K. (2025). Trajectory design for handheld mobile laser scanning in complex natural forests: a simulation approach. ISPRS Journal of Photogrammetry and Remote Sensing, 230, 524–546. doi:10.1016/j.isprsjprs.2025.09.025.

  22. López, A., Ogayar, C. J., Segura, R. J., & Casas-Rosa, J. C. (2025). Enhancing LiDAR point cloud generation with BRDF-based appearance modelling. ISPRS Journal of Photogrammetry and Remote Sensing, 222, 79–98. doi:10.1016/j.isprsjprs.2025.02.010.

  23. Park, S., Fei, S., & Habib, A. (2025). Forest Scan Planning for an Under-Canopy Mobile LiDAR Platform using Airborne Point Cloud. In IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, 1727–1731. IEEE, Brisbane, Australia. doi:10.1109/IGARSS55030.2025.11243961.

  24. Park, S., Fei, S., & Habib, A. (2025). UAV-assisted scan planning for improved forest inventory using a mobile backpack LiDAR system. Computers and Electronics in Agriculture, 239, 111147. doi:10.1016/j.compag.2025.111147.

  25. She, Y., Blake, A., Coomes, D., & Keshav, S. (2025). Scaling Up Forest Vision with Synthetic Data. arXiv. doi:10.48550/ARXIV.2509.11201.

  26. Tabernig, R., Albert, W., Weiser, H., & Höfle, B. (2025). Towards in-situ near real-time 3D environmental monitoring and geospatial point cloud analysis with open-source software. In AGIT Conference, 102. Universitätsbibliothek Salzburg. doi:10.25598/AGIT/2025-48.

  27. Tabernig, R., Albert, W., Weiser, H., Fritzmann, P., Anders, K., Rutzinger, M., & Höfle, B. (2025). Temporal aggregation of point clouds improves permanent laser scanning of landslides in forested areas. Science of Remote Sensing, 12, 100254. doi:10.1016/j.srs.2025.100254.

  28. Tabernig, R., Albert, W., Weiser, H., & Höfle, B. (2025). A hierarchical approach for near real-time 3D surface change analysis of permanent laser scanning point clouds. In 6th Joint International Symposium on Deformation Monitoring (JISDM 2025), Karlsruhe. doi:10.5445/IR/1000180377.

  29. Weiser, H., Albert, W., & Höfle, B. (2025). Non-rigid registration of wind-affected terrestrial laser scanning point clouds of trees using deep learning. Heidelberg University Library. Presentation at the Silvilaser conference 2025, Québec City, Canada. doi:10.11588/HEIDOK.00037493.

  30. Weiser, H., Albert, W., Tabernig, R., & Höfle, B. (2025). Virtual Laser Scanning of Dynamic Scenes (VLS-4D): A Novel Opportunity for Advancing 3D Forest Monitoring. In EGU General Assembly 2025, volume EGU25. doi:10.5194/egusphere-egu25-1560.

  31. Xia, J., Ma, S., Luan, G., Dong, P., Geng, R., Zou, F., Yin, J., & Zhao, Z. (2025). An Improved Method for Single Tree Trunk Extraction Based on LiDAR Data. Remote Sensing, 17(7), 1271. doi:10.3390/rs17071271.

  32. Bornand, A., Abegg, M., Morsdorf, F., & Rehush, N. (2024). Completing 3D point clouds of individual trees using deep learning. Methods in Ecology and Evolution, 15(11), 2010–2023. doi:10.1111/2041-210X.14412.

  33. Bryson, M., Ravendran, A., Mercier, C., Frickey, T., Jayathunga, S., Pearse, G., & Hartley, R. J.L. (2024). Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees. ISPRS Open Journal of Photogrammetry and Remote Sensing, 14, 100078. doi:10.1016/j.ophoto.2024.100078.

  34. Cai, S., Zhang, W., Zhang, S., Yu, S., & Liang, X. (2024). Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data. Remote Sensing of Environment, 306, 114121. doi:10.1016/j.rse.2024.114121.

  35. Chen, Z., Shi, Y., Nan, L., Xiong, Z., & Zhu, X. X. (2024). PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 693–706. doi:10.1016/j.isprsjprs.2024.09.031.

  36. Collins, F. C., Braun, A., & Borrmann, A. (2024). Finding Geometric and Topological Similarities in Building Elements for Large-Scale Pose Updates in Scan-vs-BIM. In Skatulla, S., & Beushausen, H., editors, Advances in Information Technology in Civil and Building Engineering, volume 357, pages 517–530. Springer International Publishing, Cham. doi:10.1007/978-3-031-35399-4_37.

  37. Esmorís, A. M., Weiser, H., Winiwarter, L., Cabaleiro, J. C., & Höfle, B. (2024). Deep learning with simulated laser scanning data for 3D point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 215, 192–213. doi:10.1016/j.isprsjprs.2024.06.018.

  38. Höfle, B., Tabernig, R., Zahs, V., Esmorís Pena, A. M., Winiwarter, L., & Weiser, H. (2024). Machine-learning based 3D point cloud classification and multitemporal change analysis with simulated laser scanning data using open source scientific software. In EGU General Assembly 2024, volume EGU24. doi:10.5194/egusphere-egu24-1261.

  39. Noichl, F., Collins, F. C., Braun, A., & Borrmann, A. (2024). Enhancing point cloud semantic segmentation in the data-scarce domain of industrial plants through synthetic data. Computer-Aided Civil and Infrastructure Engineering, 39(10), 1530–1549. doi:10.1111/mice.13153.

  40. Schäfer, J., Winiwarter, L., Weiser, H., Höfle, B., Schmidtlein, S., Novotný, J., Krok, G., Stereńczak, K., Hollaus, M., & Fassnacht, F. E. (2024). CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography. European Journal of Remote Sensing, 57(1), 2396932. doi:10.1080/22797254.2024.2396932.

  41. Schäfer, J., Winiwarter, L., Weiser, H., Novotný, J., Höfle, B., Schmidtlein, S., Henniger, H., Krok, G., Stereńczak, K., & Fassnacht, F. E. (2024). Assessing the potential of synthetic and Ex Situ airborne laser scanning and ground plot data to train forest biomass models. Forestry: An International Journal of Forest Research, 97(4), 512–530. doi:10.1093/forestry/cpad061.

  42. Tabernig, R., Zahs, V., Weiser, H., & Höfle, B. (2024). Simulating 4D scenes of rockfall and landslide activity for improved 3D point cloud-based change detection using machine learning. In EGU General Assembly 2024, volume EGU24. doi:10.5194/egusphere-egu24-1613.

  43. Tang, S., Ao, Z., Li, Y., Huang, H., Xie, L., Wang, R., Wang, W., & Guo, R. (2024). TreeNet3D : A large scale tree benchmark for 3D tree modeling, carbon storage estimation and tree segmentation. International Journal of Applied Earth Observation and Geoinformation, 130, 103903. doi:10.1016/j.jag.2024.103903.

  44. Weiser, H., Esmorís Pena, A. M., & Höfle, B. (2024). How Tree Movement Influences Tree Metrics Derived from Laser Scanning Point Clouds. In EGU General Assembly 2024, volume EGU 2024. doi:10.5194/egusphere-egu24-1633.

  45. Yang, T., Zou, Y., Yang, X., & Del Rey Castillo, E. (2024). Domain knowledge-enhanced region growing framework for semantic segmentation of bridge point clouds. Automation in Construction, 165, 105572. doi:10.1016/j.autcon.2024.105572.

  46. Zahs, V., Höfle, B., Federer, M., Weiser, H., Tabernig, R., & Anders, K. (2024). Automatic Classification of Surface Activity Types from Geographic 4D Monitoring Combining Virtual Laser Scanning, Change Analysis and Machine Learning. In EGU General Assembly. doi:10.5194/egusphere-egu24-1640.

  47. Comesaña-Cebral, L., Martínez-Sánchez, J., Seoane, A. N., & Arias, P. (2024). Transport Infrastructure Management Based on LiDAR Synthetic Data: A Deep Learning Approach with a ROADSENSE Simulator. Infrastructures, 9(3), 58. doi:10.3390/infrastructures9030058.

  48. González-Quiñones, J. J., Polidori, L., Ariza-López, F. J., Ureña-Cámara, M. A., & Reinoso-Gordo, J. F. (2024). Influence of tree density and terrain slope on ground point density in LiDAR point clouds: a simulation-based study with Helios++. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-3-2024, 197–202. doi:10.5194/isprs-archives-XLVIII-3-2024-197-2024.

  49. Lytkin, S., Badenko, V., Fedotov, A., Vinogradov, K., Chervak, A., Milanov, Y., & Zotov, D. (2023). Saint Petersburg 3D: Creating a Large-Scale Hybrid Mobile LiDAR Point Cloud Dataset for Geospatial Applications. Remote Sensing, 15(11), 2735. doi:10.3390/rs15112735.

  50. Neumann, M., Borrmann, D., & Nüchter, A. (2023). Semantic Classification in Uncolored 3D Point Clouds Using Multiscale Features. In Petrovic, I., Menegatti, E., & Marković, I., editors, Intelligent Autonomous Systems 17, volume 577, pages 342–359. Springer Nature Switzerland, Cham. doi:10.1007/978-3-031-22216-0_24.

  51. Schäfer, J., Weiser, H., Winiwarter, L., Höfle, B., Schmidtlein, S., & Fassnacht, F. E. (2023). Generating synthetic laser scanning data of forests by combining forest inventory information, a tree point cloud database and an open-source laser scanning simulator. Forestry: An International Journal of Forest Research, 96(5), 653–671. doi:10.1093/forestry/cpad006.

  52. Stocker, O., Kouhi, R. M., Guilbert, E., Ferraz, A., & Badard, T. (2023). Investigating the Impact of Point Cloud Density on Semantic Segmentation Performance Using Virtual Lidar in Boreal Forest. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 978–981. IEEE, Pasadena, CA, USA. doi:10.1109/IGARSS52108.2023.10282100.

  53. Winiwarter, L., Anders, K., Czerwonka-Schröder, D., & Höfle, B. (2023). Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering. Earth Surface Dynamics, 11(4), 593–613. doi:10.5194/esurf-11-593-2023.

  54. Zahs, V., Anders, K., Kohns, J., Stark, A., & Höfle, B. (2023). Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data. International Journal of Applied Earth Observation and Geoinformation, 122, 103406. doi:10.1016/j.jag.2023.103406.

  55. Eickeler, F., & Borrmann, A. (2022). Enhancing Railway Detection by Priming Neural Networks with Project Exaptations. Remote Sensing, 14(21), 5482. doi:10.3390/rs14215482.

  56. Kosse, S., Vogt, O., Wolf, M., König, M., & Gerhard, D. (2022). Digital Twin Framework for Enabling Serial Construction. Frontiers in Built Environment, 8, 864722. doi:10.3389/fbuil.2022.864722.

  57. Liu, X., Ma, Q., Wu, X., Hu, T., Liu, Z., Liu, L., Guo, Q., & Su, Y. (2022). A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. Remote Sensing of Environment, 282, 113280. doi:10.1016/j.rse.2022.113280.

  58. Mafipour, M. Saeed., Alici, C., Shakeel, S. S., & Kalkavan, A. (2022). Semantic Segmentation of Real and Synthetic Point Cloud Data for Digital Twinning of Bridges. In Proceedings of 33. Forum Bauinformatik, 378–385. Munich, Germany. URL: https://mediatum.ub.tum.de/doc/1688410/2g3vksqmxngg53qm408d5c1m1.Mafipour%20et%20Al.%202022.pdf, doi:10.14459/2022MD1686600.

  59. Richter, K., & Maas, H.-G. (2022). Radiometric enhancement of full-waveform airborne laser scanner data for volumetric representation in environmental applications. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 510–524. doi:10.1016/j.isprsjprs.2021.10.021.

  60. Wang, D., Puttonen, E., & Casella, E. (2022). PlantMove: A tool for quantifying motion fields of plant movements from point cloud time series. International Journal of Applied Earth Observation and Geoinformation, 110, 102781. doi:10.1016/j.jag.2022.102781.

  61. Winiwarter, L., Anders, K., Schröder, D., & Höfle, B. (2022). VIRTUAL LASER SCANNING OF DYNAMIC SCENES CREATED FROM REAL 4D TOPOGRAPHIC POINT CLOUD DATA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2022, 79–86. doi:10.5194/isprs-annals-V-2-2022-79-2022.

  62. Winiwarter, L., Esmorís Pena, A. M., Weiser, H., Anders, K., Martínez Sánchez, J., Searle, M., & Höfle, B. (2022). Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic full-waveform 3D laser scanning. Remote Sensing of Environment, 269, 112772. doi:10.1016/j.rse.2021.112772.

  63. Winiwarter, L., Esmorís Pena, A. M., Zahs, V., Weiser, H., Searle, M., Anders, K., & Höfle, B. (2022). Virtual Laser Scanning using HELIOS++ - Applications in Machine Learning and Forestry. In EGU General Assembly. doi:10.5194/egusphere-egu22-8671.

  64. Lecigne, B., Delagrange, S., & Taugourdeau, O. (2021). Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth. Forests, 12(4), 391. doi:10.3390/f12040391.

  65. Li, L., Mu, X., Soma, M., Wan, P., Qi, J., Hu, R., Zhang, W., Tong, Y., & Yan, G. (2021). An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing, 59(4), 3547–3566. doi:10.1109/TGRS.2020.3018643.

  66. Noichl, F., Braun, A., & Borrmann, A. (2021). "BIM-to-Scan" for Scan-to-BIM: Generating Realistic Synthetic Ground Truth Point Clouds based on Industrial 3D Models. In 2021 European Conference on Computing in Construction, 164–172. doi:10.35490/EC3.2021.166.

  67. Reitmann, S., Neumann, L., & Jung, B. (2021). BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data. Sensors, 21(6), 2144. doi:10.3390/s21062144.

  68. Weiser, H., Winiwarter, L., Anders, K., Fassnacht, F. E., & Höfle, B. (2021). Opaque voxel-based tree models for virtual laser scanning in forestry applications. Remote Sensing of Environment, 265, 112641. doi:10.1016/j.rse.2021.112641.

  69. Wu, B., Zheng, G., Chen, Y., & Yu, D. (2021). Assessing inclination angles of tree branches from terrestrial laser scan data using a skeleton extraction method. International Journal of Applied Earth Observation and Geoinformation, 104, 102589. doi:10.1016/j.jag.2021.102589.

  70. Backes, D., Smigaj, M., Schimka, M., Zahs, V., Grznárová, A., & Scaioni, M. (2020). RIVER MORPHOLOGY MONITORING OF A SMALL-SCALE ALPINE RIVERBED USING DRONE PHOTOGRAMMETRY AND LIDAR. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020, 1017–1024. doi:10.5194/isprs-archives-XLIII-B2-2020-1017-2020.

  71. Park, M., Baek, Y., Dinare, M., Lee, D., Park, K.-H., Ahn, J., Kim, D., Medina, J., Choi, W.-J., Kim, S., Zhou, C., Heo, J., & Lee, K. (2020). Hetero-integration enables fast switching time-of-flight sensors for light detection and ranging. Scientific Reports, 10(1), 2764. doi:10.1038/s41598-020-59677-x.

  72. Wang, D. (2020). Unsupervised semantic and instance segmentation of forest point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 165, 86–97. doi:10.1016/j.isprsjprs.2020.04.020.

  73. Wang, D., Schraik, D., Hovi, A., & Rautiainen, M. (2020). Direct estimation of photon recollision probability using terrestrial laser scanning. Remote Sensing of Environment, 247, 111932. doi:10.1016/j.rse.2020.111932.

  74. Zhang, Z., Li, J., Guo, Y., Yang, C., & Wang, C. (2020). 3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4762–4772. doi:10.1109/TITS.2019.2946259.

  75. Zhu, X., Liu, J., Skidmore, A. K., Premier, J., & Heurich, M. (2020). A voxel matching method for effective leaf area index estimation in temperate deciduous forests from leaf-on and leaf-off airborne LiDAR data. Remote Sensing of Environment, 240, 111696. doi:10.1016/j.rse.2020.111696.

  76. Lin, C.-H., & Wang, C.-K. (2019). POINT DENSITY SIMULATION FOR ALS SURVEY. In Proceedings of the 11th International Conference on Mobile Mapping Technology (MMT2019), 157–160. Shenzhen, China.

  77. Liu, J., Skidmore, A. K., Wang, T., Zhu, X., Premier, J., Heurich, M., Beudert, B., & Jones, S. (2019). Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 208–220. doi:10.1016/j.isprsjprs.2019.01.005.

  78. Liu, J., Wang, T., Skidmore, A. K., Jones, S., Heurich, M., Beudert, B., & Premier, J. (2019). Comparison of terrestrial LiDAR and digital hemispherical photography for estimating leaf angle distribution in European broadleaf beech forests. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 76–89. doi:10.1016/j.isprsjprs.2019.09.015.

  79. Martínez Sánchez, J., Váquez Álvarez, Á., López Vilariño, D., Fernández Rivera, F., Cabaleiro Domínguez, J. C., & Fernández Pena, T. (2019). Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation. Remote Sensing, 11(19), 2256. doi:10.3390/rs11192256.

  80. Previtali, M., Díaz-Vilariño, L., Scaioni, M., & Frías, E. (2019). Evaluation of the Expected Data Quality in Laser Scanning Surveying of Archaeological Sites. In Proc. of “2019 IMEKO TC-4 Int. Conf. on Metrology for Archeology and Cultural Heritage (METROARCHEO2019)", 19–24. Florence, Italy. URL: http://hdl.handle.net/11311/1124569.

  81. Xiao, W., Zaforemska, A., Smigaj, M., Wang, Y., & Gaulton, R. (2019). Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data. Remote Sensing, 11(11), 1263. doi:10.3390/rs11111263.

  82. Hämmerle, M., Lukač, N., Chen, K.-C., Koma, Zs., Wang, C.-K., Anders, K., & Höfle, B. (2017). SIMULATING VARIOUS TERRESTRIAL AND UAV LIDAR SCANNING CONFIGURATIONS FOR UNDERSTORY FOREST STRUCTURE MODELLING. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W4, 59–65. doi:10.5194/isprs-annals-IV-2-W4-59-2017.

  83. Rebolj, D., Pučko, Z., Babič, N. Č., Bizjak, M., & Mongus, D. (2017). Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. Automation in Construction, 84, 323–334. doi:10.1016/j.autcon.2017.09.021.

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