HELIOS++ - Heidelberg LiDAR Operations Simulator

HELIOS++ is hosted on GitHub with an extensive wiki.

News

Stay up-to-date by following HELIOS via our GIScience News Blog and on Bluesky and LinkedIn: #HELIOS #3DGeo.

New release: HELIOS++ v2.0.0.
From this version on, HELIOS++ is on Conda-forge and can be installed with:
conda install -c conda-forge helios
or
mamba install -c conda-forge helios
Check out our Blogpost.

Successful proposal: Fostering a community-driven and sustainable HELIOS++ scientific software: The 3DGeo Group and the Scientific Software Center (SSC) of Heidelberg University have been successful with their proposal in the DFG call “Research Software – Quality assured and re-usable”. The main objective of the new project is to bring HELIOS++ to a professional level of software development and quality and to establish sustainable institutional structures (see press release)

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.

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}
}

Secondary paper on virtual laser scanning simulation with HELIOS++ as a high performance computing challenge:

• Esmorís, A. M., Yermo, M., Weiser, H., Winiwarter, L., Höfle, B. & Rivera, F.F. (2022): Virtual LiDAR simulation as a high performance computing challenge: Towards HPC HELIOS++. IEEE Access 10, pp. 105052-105073. DOI: 10.1109/ACCESS.2022.3211072.

Research

HELIOS++ and its predecessor have been used extensively in different research:

3DGeo Publications

2024

• 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). DOI: 10.1080/22797254.2024.2396932.
• 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.

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.
• 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. Vol. 2023, pp. 1-19. DOI: 10.1093/forestry/cpad006
• 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.
• 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. Vol. 122, pp. 103406. DOI: 10.1016/j.jag.2023.103406

2022

• Esmorís, A. M., Yermo, M., Weiser, H., Winiwarter, L., Höfle, B. & Rivera, F.F. (2022): Virtual LiDAR simulation as a high performance computing challenge: Towards HPC HELIOS++. IEEE Access 10, pp. 105052-105073. DOI: https://doi.org/10.1109/ACCESS.2022.3211072.
• Searle, M., Weiser, H., Winiwarter, L. & Höfle, B. (2022): Simulation von Laserscanning mit AEOS, dem QGIS Plugin für HELIOS++. FOSSGIS 2022. Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, Open Data und OpenStreetMap. pp. 203-204. FOSSGIS e.V..
• Searle, M. & Weiser, H. (2022): Simulation von Laserscanning mit AEOS, dem QGIS Plugin für HELIOS++ [Video]. FOSSGIS 2022. Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, Open Data und OpenStreetMap. DOI: https://doi.org/10.5446/56808
• 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.

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. Vol. 265, pp. 112641. DOI: 10.1016/j.rse.2021.112641.

2020

• 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 lidar. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, pp. 1017-1024. DOI: 10.5194/isprs-archives-XLIII-B2-2020-1017-2020

2019

• Schäfer, J., Faßnacht, F., Höfle, B. & Weiser, H.(2019): Das SYSSIFOSS-Projekt: Synthetische 3D-Fernerkundungsdaten für verbesserte Waldinventurmodelle. 2. Symposium zur angewandten Satellitenerdbeoachtung, Cologne, Germany, pp.1-1.

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.

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.
• 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. 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

2025

• 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, 79-98. DOI: 10.1016/j.isprsjprs.2025.02.010.

2024

• Chen, Z., Shi, Y., Nan, L., Xiong, Z. & Zhu, X. (2024): PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds. ISPRS Journal of Photogrammetry and Remote Sensing 218(A), 693-706. DOI: 10.1016/j.isprsjprs.2024.09.031.
• 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.
• Pečur, T., Bosché, F., Cerniauskas, G., Mill, F., Sherlock, A. & Yu, N. (2024): Prototype pipeline modelling using interval scanning point clouds. Advances in Manufacturing. DOI: 10.1007/s40436-024-00515-y.
• 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.
• 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, Vol. 357, Springer, Cham. DOI: 10.1007/978-3-031-35399-4_37.

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. In: Remote Sensing 15(11), 2735. DOI: 10.3390/rs15112735.
• 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. 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 978-981, DOI: 10.1109/IGARSS52108.2023.10282100.

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: https://doi.org/10.1016/j.jag.2022.102781.

2021

• 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). 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: 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. 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. 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., 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. 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. 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, 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. IEEE Transactions on Intelligent Transportation Systems, pp. 1-11. DOI: 10.1109/TITS.2019.2946259.

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.

Winiwarter et al. (2022) 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.

HELIOS++ software modules (platform, scanner, scene, survey) and LiDAR simulation sequence.

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!

Projects using HELIOS++

AImon5.0 - Real-time monitoring of gravitational mass movements for critical infrastructure risk management with AI-assisted 3D metrology

VirtuaLearn3D - Virtual Laser Scanning for Machine Learning Algorithms in Geographic 3D Point Cloud Analysis

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