Research using HELIOS - 3dgeo-heidelberg/helios GitHub Wiki

2026

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.

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

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

Gao, Y., Liang, W., Li, M., Chen, L., Zou, X. & Zhan, J. (2026): DBE-Net: A dual-branch bounding box-embedding network for individual tree segmentation from LiDAR. International Journal of Applied Earth Observation and Geoinformation, 151, 105381. DOI: 10.1016/j.jag.2026.105381

González-Quiñones, J. J., Ariza-López, F. J., Rodríguez-Avi, J., Ureña-Cámara, M. A. & Reinoso-Gordo, J. F. (2026): Optimización basada en simulación de la geometría piramidal truncada de Dianas de Control para el control de calidad de nubes de puntos generadas por LiDAR a bordo de VANT. Revista Cartográfica, (113), 71–88. 10.35424/rcarto.i113.6112

Hulskemper, D., Weiser, H., Tabernig, R., Höfle, B., De Jong, T. & Lindenbergh, R. (2026): Low-cost Terrestrial Laser Scanners for Permanent Monitoring of Beach-Dune Systems. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XI-1–2026, 91–99. DOI 10.5194/isprs-annals-XI-1-2026-91-2026

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

Lee, J. K., Lee, U. C. & Ma, J. W. (2026): A Systematic Study on Instance-Level Bridge Point Cloud Segmentation under Cross-Type and Synthetic-Real Training Conditions. In Proceedings of the 43rd ISARC, Singapore, 1984–1991. DOI: 10.22260/ISARC2026/0253.

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

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

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. 10.1016/j.rse.2026.115467

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

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. DOI: 10.48550/arXiv.2603.04683

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. 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 5026–5036. DOI: 10.1109/WACV61042.2026.00488

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

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

Weiser, H. & Höfle, B. (2026): Neue Möglichkeiten im 3D-Umweltmonitoring durch virtuelles Laserscanning von dynamischen Szenen (VLS-4D). 46. Wissenschaftlich-Technische Jahrestagung der DGPF, 25.-27. März in Darmstadt. Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V., Vol. 34, pp. 348–356.

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., … 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

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

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

2025

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

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

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

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

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

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

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, pp. 100254. DOI: 10.1016/j.srs.2025.100254

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, pp. 25-32. DOI: 10.5194/isprs-annals-X-G-2025-25-2025

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. AGIT Conference 2025, pp. 202-202. DOI: 10.25598/agit/2025-48

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. 6th Joint International Symposium on Deformation Monitoring (JISDM), 6, pp. 1-9. DOI: 10.5445/IR/1000180377

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. EGU General Assembly 2025, EGU25, pp. 1-2. DOI: 10.5194/egusphere-egu25-1560

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.

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). DOI: 10.1080/15481603.2025.2474836.

López, A., Ogayar, C., 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, pp. 79-98. DOI: 10.1016/j.isprsjprs.2025.02.010.

2024

Chen, Z., Shi, Y., Nan, L., Xiong, Z. & Zhu, X. (2023): PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds. ISPRS Journal of Photogrammetry and Remote Sensing 218(A), pp. 693-706. DOI: 10.1016/j.isprsjprs.2024.09.031.

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. DOI: 10.1016/j.ophoto.2024.100078.

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.

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, 2010–2023. DOI: 10.1111/2041-210X.14412.

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, pp. 1-18. DOI: 10.1080/22797254.2024.2396932.

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.

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.

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. Vol. 215, pp. 192-213. DOI: 10.1016/j.isprsjprs.2024.06.018.

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. EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1261. DOI: 10.5194/egusphere-egu24-1261.

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. EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1613. DOI: 10.5194/egusphere-egu24-1613.

Weiser, H., Esmorís Pena, A. M. & Höfle, B. (2024): How Tree Movement Influences Tree Metrics Derived from Laser Scanning Point Clouds. EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1633. DOI: 10.5194/egusphere-egu24-1633.

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. EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1640. DOI: 10.5194/egusphere-egu24-1640.

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(1), 114121. DOI: 10.1016/j.rse.2024.114121.

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.

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, 1–20. DOI: 10.1111/mice.13153.

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. (eds): Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering 357, Springer, Cham. DOI: 10.1007/978-3-031-35399-4_37.

2023

Schäfer, J., Winiwarter, L., Weiser, H., Novotný, J., Höfle, B., Schmidtlein, S., Henniger, H., Krok, G., Stereńczak, K. & Fassnacht, F.E. (2023): 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. cpad061, pp. 1-19. DOI: 10.1093/forestry/cpad061

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. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 978-981. DOI: 10.1109/IGARSS52108.2023.10282100.

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, pp. 103406. DOI: 10.1016/j.jag.2023.103406.

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, pp. 593-613. DOI: 10.5194/esurf-11-593-2023.

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.

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). DOI: 10.1093/forestry/cpad006.

Neumann, M., Borrmann, D., Nüchter, A. (2023). Semantic Classification in Uncolored 3D Point Clouds Using Multiscale Features. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems 577. Springer, Cham. DOI: 10.1007/978-3-031-22216-0_24.

2022

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

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

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. DOI: 10.1016/j.rse.2022.113280.

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. DOI: 10.1016/j.isprsjprs.2021.10.021.

Saeed Mafipour, M., Alici, C., Saadat Shakeel, S., Kalkavan, A. (2022): Semantic Segmentation of Real and Synthetic Point Cloud Data for Digital Twinning of Bridges. Proceedings of 33. ForumBauinformatik, 7–9 September 2022, pp. 378-386. DOI: 10.14459/2022md1686600.

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. DOI: 10.1016/j.jag.2022.102781.

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, pp. 79-86. DOI: 10.5194/isprs-annals-V-2-2022-79-2022.

2021

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, pp. 112641. DOI: 10.1016/j.rse.2021.112641.

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). DOI: 10.3390/f12040391.

Noichel, 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. 2021 European Conference on Computing in Construction, 27-28 July 2021, pp. 1-9. DOI: 10.35490/EC3.2021.166. Link to conference video.

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.

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. DOI: 10.1016/j.jag.2021.102589.

2020

Li, L., Mu, X., Soma, M., Wan, P., Qi, J., Hu, R., Zhang, W., Tong, Y. & Yan, G. (2020): An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing. DOI: 10.1109/TGRS.2020.3018643.

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. Sci Rep 10, 2764 (2020), pp. 1-8. DOI: 10.1038/s41598-020-59677-x.

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

Wang, D., Schraik, D., Hovi, A. & Rautiainen, M. (2020): Direct estimation of photon recollision probability using terrestrial laser scanning. Remote Sensing of Environment 247 (2020), pp. 1-12. DOI: 10.1016/j.rse.2020.111932.

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. DOI: 10.1016/j.rse.2020.111696.

2019

Lin, C.-H. & Wang, C.-K. (2019): Point Density Simulation for ALS Survey. Proceedings of the 11th International Conference on Mobile Mapping Technology (MMT2019), Shenzhen, China. pp. 157-160.

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, pp. 208-220. DOI: 10.1016/j.isprsjprs.2019.01.005.

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, pp. 76-89. DOI: 10.1016/j.isprsjprs.2019.09.015.

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), pp. 23 (2256). DOI: 10.3390/rs11192256.

Previtali, M., Díaz-Vilariño, L., Scaioni, M. & Frías Nores, E. (2019): Evaluation of the Expected Data Quality in Laser Scanning Surveying of Archaeological Sites. 4th International Conference on Metrology for Archaeology and Cultural Heritage, Florence, Italy, 4-6 December 2019, pp. 19-24.

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), pp. 19 (1263). DOI: 10.3390/rs11111263.

Zhang, Z., Li, J., Guo, Y., Yang, C., & Wang, C. (2019): 3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2019.2946259.

2017

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 Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, pp. 59-65. DOI: 10.5194/isprs-annals-IV-2-W4-59-2017.

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, pp. 323-334. DOI: 10.1016/j.autcon.2017.09.021.

2016

Bechtold, S., Hämmerle, M. & Höfle, B. (2016): Simulated full-waveform laser scanning of outcrops for development of point cloud analysis algorithms and survey planning: An application for the HELIOS lidar simulation framework. Proceedings of the 2nd Virtual Geoscience Conference, Bergen, Norway, 21-23 September 2016, pp. 57-58.