-
Notifications
You must be signed in to change notification settings - Fork 44
Research using HELIOS
Chen, Z., Shi, Y., Nan, L., Xiong, Z. & Zhu, X. (2023): PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds. [Preprint] DOI: 10.48550/arXiv.2307.08636 [cs.CV].
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. In: 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. In: Forestry: An International Journal of Forest Research, cpad006, DOI: 10.1093/forestry/cpad006.
Eickeler, F. & Borrmann, A. (2022): Enhancing Railway Detection by Priming Neural Networks with Project Exaptations. In: 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. In: 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. In: 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. In: 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, p. 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. In: 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. Vol. V-2-2022, pp. 79-86. DOI: 10.5194/isprs-annals-V-2-2022-79-2022.
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. In: 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. In: 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. In: International Journal of Applied Earth Observation and Geoinformation 104. DOI: 10.1016/j.jag.2021.102589.
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. In: 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. In: 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. In: 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. In: 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. In: Remote Sensing of Environment, 240. DOI: 10.1016/j.rse.2020.111696.
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), 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. In: 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. In: 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. In: 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. In: 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. In: 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. In: IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2019.2946259.
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. In: 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. In: Automation in Construction, 84, pp. 323-334. DOI: 10.1016/j.autcon.2017.09.021.
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. In: Proceedings of the 2nd Virtual Geoscience Conference, Bergen, Norway, 21-23 September 2016, pp. 57-58.