Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project number: 528521476, call: „Research Software – Quality Assured and Re-usable”




Prof. Bernhard Höfle
Institute of Geography, Heidelberg University, Germany

Project partners

Dr. Dominic Kempf
Scientific Software Center (SSC), Heidelberg University, Germany

Team Heidelberg University

Fostering a community-driven and sustainable HELIOS++ scientific software


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Press release: The successfull proposal of our new project has been featured in Heidelberg University's News Room - see the full press release)


The primary objective of this project is to elevate HELIOS++, our laser scanning simulation software, to a professional level of software development and quality and to establish sustainable institutional structures. This will be achieved by integrating the Scientific Software Center (SSC) at Heidelberg University into the development process, while fostering a larger, decentralized user and developer community.

In this project, we will focus on improvements in software architecture, quality assurance, reproducibility, usability, interoperability, and active community building.

This will benefit a large number of current and future users who integrate HELIOS++ as an important module in their research and data analysis processes.


HELIOS++ is a versatile open-source scientific software for virtual laser scanning (VLS). It enables researchers and professionals to simulate point cloud data, replicating what a laser scanner would capture. This software has various practical applications in the field of geospatial research and beyond:

  • Data Acquisition Planning: Researchers can use HELIOS++ to plan data acquisition strategies and evaluate the feasibility of their scanning projects.
  • Teaching: HELIOS++ serves as an educational tool, helping students and professionals better grasp the concepts of laser scanning.
  • Sensitivity Analyses: By simulating different scenarios, researchers can conduct sensitivity analysis to assess how variations impact the outcomes of laser scanning.
  • Machine Learning Data: HELIOS++ is valuable for generating training datasets for supervised machine learning algorithms, as is the research focus of VirtuaLearn3D.
  • Algorithm and Sensor Development: Researchers and developers can employ HELIOS++ to test and refine novel algorithms and sensor technologies without the need for costly physical equipment.

Key features of HELIOS++ as a scientific software:

  • Open Source: HELIOS++ is an open-source project hosted on GitHub, fostering transparency, collaboration, and the exchange of knowledge with the scientific community.
  • Modular Architecture: The modular structure provides researchers with the flexibility to adapt and customize the different components of laser scanning (survey, platform, scanner scene) as needed for their research purpose.
  • Command Line and Python Interface: HELIOS++ can be used as a command line tool or with the Python bindings pyhelios
  • Comprehensive Documentation: HELIOS++ has an extensive documentation in the form of a Wiki and a gallery of examples.


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

About our project partner

Scientific Software Center (SSC)

The SSC is dedicated to supporting and improving scientific software development to ensure reproducibility, transparency and sustainability. It does so by software engineering training for scientists, collaboration on software development, and outreach activities.

Related projects
  • HELIOS++: Heidelberg LiDAR Operations Simulator
  • VirtuaLearn3D: Virtual Laser Scanning for Machine Learning Algorithms in Geographic 3D Point Cloud Analysis
Related publications
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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