HELIOS++ - Heidelberg LiDAR Operations Simulator
News
Stay up-to-date by following HELIOS on ResearchGate. Research news can be found in our GIScience News Blog and on Twitter: #HELIOS #3DGeo.
General Information
In 2020, HELIOS++ replaced the former version of HELIOS with a modern implementation in C++11, including Python bindings to allow easy use in existing workflows. The code and ready-for-use precompiled versions are hosted on GitHub. We invite interested researchers and developers to contribute to further development of this project by submitting pull requests. We also host an extensive wiki, where the complete functionality of HELIOS++ is documented.
Outreach
At the 2022 FOSSGISS conference on free open source software for geographic information systems, we presented AEOS, the QGIS Plugin that enables the usage of HELIOS++ in one of the most widely used GIS applications. Check out the demo session here and learn how to simulate your own point clouds!
Literature & How to cite HELIOS++
More information on HELIOS++ is available in our publication. If you use HELIOS++ in your work, please cite:
- Winiwarter, L., Esmorís Pena, A., Weiser, H., Anders, K., Martínez Sanchez, 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. Vol. 269. DOI: 10.1016/j.rse.2021.112772
Citation as BibTex:
@article{heliosPlusPlus,
title = {Virtual laser scanning with HELIOS++:
A novel take on ray tracing-based simulation
of topographic full-waveform 3D laser scanning},
journal = {Remote Sensing of Environment},
volume = {269},
year = {2022},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2021.112772},
author = {Lukas Winiwarter and Alberto Manuel {Esmorís Pena}
and Hannah Weiser and Katharina Anders
and Jorge {Martínez Sánchez} and Mark Searle and Bernhard Höfle}
}
Research
HELIOS++ and its predecessor have been used extensively in different research:
- Searle, M., Weiser, H., Winiwarter, L. & Höfle, B. (2022): Simulation von Laserscanning mit AEOS, dem QGIS Plugin für HELIOS++. In: FOSSGIS 2022. Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, Open Data und OpenStreetMap. pp. 203-204. FOSSGIS e.V..
- Weiser, H., Searle, M., Winiwarter, L. & Höfle, B. (2022): Laserscanning simulieren mit HELIOS++ - Eine praktische Einführung. In: FOSSGIS 2022. Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, Open Data und OpenStreetMap. pp. 175-176. FOSSGIS e.V..
- 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.
- Winiwarter, L., Esmorís Pena, A. M., Zahs, V., Weiser, H., Searle, M., Anders, K., and Höfle, B. (2022): Virtual Laser Scanning using HELIOS++ - Applications in Machine Learning and Forestry. EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022. DOI: 10.5194/egusphere-egu22-8671.
- 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. Vol. 265, pp. 112641. DOI: 10.1016/j.rse.2021.112641.
- Backes, D., Smigaj, M., Schimka, M., Zahs, V., Grznárová, A., and Scaioni, M (2020): River morphology monitoring of a small-scale alpine riverbed using drone photogrammetry and lidarIn: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, pp. 1017-1024. DOI: 10.5194/isprs-archives-XLIII-B2-2020-1017-2020
- Schäfer, J., Faßnacht, F., Höfle, B. & Weiser, H.(2019): Das SYSSIFOSS-Projekt: Synthetische 3D-Fernerkundungsdaten für verbesserte Waldinventurmodelle.In: 2. Symposium zur angewandten Satellitenerdbeoachtung, Cologne, Germany, pp.1-1.
- 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.
- 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.
- Bechtold, S. & Höfle, B. (2016): HELIOS: A Multi-purpose LiDAR Simulation Framework for Research, Planning and Training of Laser Scanning Operations With Airborne, Ground-based Mobile and Stationary Platforms. In: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, pp. 161-168. DOI: 10.5194/isprs-annals-III-3-161-2016.
Publications from Other Research Groups
- 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.
- 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.
- 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: https://doi.org/10.1016/j.jag.2022.102781.
- 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.
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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). 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: https://doi.org/10.1038/s41598-020-59677-x.
- Schlager, B., Muckenhuber, S., Schmidt, S., Holzer, H. et al. (2020): State-of-the-Art Sensor Models for Virtual Testing of Advanced Driver Assistance Systems/Autonomous Driving Functions. In: SAE International Journal of Connected and Automated Vehicles 3 (3), pp. 233-261. DOI: 10.4271/12-03-03-0018.
- 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., Álvarez, Á., Vilariño, D., Rivera, F., Cabaleiro, J. & Pena, T. (2019): Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation. In: Remote Sensing 11, pp. 1-23. 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, pp. 1-19. DOI: https://doi.org/10.3390/rs11111263.
- Zhang, Z., Li, J., Guo, Y. & Yang, C. (2019): 3D Highway Curve Reconstruction From Mobile Laser Scanning Point Clouds. In: IEEE Transactions on Intelligent Transportation Systems, pp. 1-11. DOI: 10.1109/TITS.2019.2946259.
- 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.
Winiwarter et al. (2021) conducted a systematic literature review based on these publications (see above).
Background
Virtual laser scanning is a tool to create simulated point cloud data, as would be acquired by a LiDAR sensor. Such data may be used to complement real data, where data acquisition is not feasible due to economical or logistic constraints or where it is impossible, e.g. when simulating a sensor that does not exist. HELIOS++ allows the simulation of laser scanning on different platforms (airborne, UAV-based, terrestrial mobile and static) and using different data types to represent the 3D scene, including triangular meshes, digital elevation rasters, voxel grids and point clouds. The implementation in C++ allows for low runtimes and efficient memory usage, while the Python bindings pyhelios enable direct use of HELIOS++ from within Python scripts.
Simulation of an airborne laser scan over a DEM of Heidelberg (USGS SRTM 3” DEM, Winiwarter et al. 2021).
Projects using HELIOS++
We investigate the close coupling of learning algorithms with virtual laser scanning and real point cloud data to use benefits of both 1) the realism of real − but sparse − training data and 2) the multitude of options of object-sensor interactions that can be generated with VLS.
SYSSIFOSS - Synthetic structural remote sensing data for improved forest inventory models
LOKI - Airborne Observation of Critical Infrastructure
