Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning
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Abstract (arXiv)
Topographic laser scanning is a remote sensing method to create detailed 3D point cloud representations of the Earth's surface. Since data acquisition is expensive, simulations can complement real data given certain premises are available: i) a model of 3D scene and scanner, ii) a model of the beam-scene interaction, simplified to a computationally feasible while physically realistic level, and iii) an application for which simulated data is fit for use. A number of laser scanning simulators for different purposes exist, which we enrich by presenting HELIOS++. HELIOS++ is an open-source simulation framework for terrestrial static, mobile, UAV-based and airborne laser scanning implemented in C++. The HELIOS++ concept provides a flexible solution for the trade-off between physical accuracy (realism) and computational complexity (runtime, memory footprint), as well as ease of use and of configuration. Unique features of HELIOS++ include the availability of Python bindings (pyhelios) for controlling simulations, and a range of model types for 3D scene representation. HELIOS++ further allows the simulation of beam divergence using a subsampling strategy, and is able to create full-waveform outputs as a basis for detailed analysis. As generation and analysis of waveforms can strongly impact runtimes, the user may set the level of detail for the subsampling, or optionally disable full-waveform output altogether. A detailed assessment of computational considerations and a comparison of HELIOS++ to its predecessor, HELIOS, reveal reduced runtimes by up to 83 %. At the same time, memory requirements are reduced by up to 94 %, allowing for much larger (i.e. more complex) 3D scenes to be loaded into memory and hence to be virtually acquired by laser scanning simulation.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 1 of its 71 references that are in the map. First posted on arXiv 21 Jan 2021.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 5; 2026: 1.
11 papers.
- 2023Neural LiDAR Fields for Novel View Synthesiscited by 65
- 2024SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning datacited by 16
- 2023PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Cloudscited by 10
- 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Datasetcited by 5
- 2025Scaling Up Forest Vision with Synthetic Datacited by 3
- 2023Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning datacited by 2
- 2025Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branchescited by 2
- 2025Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT datasetcited by 2
- 2025TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understandingcited by 2
- 2022Synthetic Point Cloud Generation for Class Segmentation Applicationscited by 0
- 2026Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space modelscited by 0
- 2026Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space models11 Mar 2026
- 2025TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understanding10 Nov 2025
- 2025Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset1 Nov 2025
- 2025Scaling Up Forest Vision with Synthetic Data14 Sep 2025
- 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset12 May 2025
- 2025Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches7 Mar 2025
- 2024SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data28 Jan 2024
- 2023PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds17 Jul 2023
- 2023Neural LiDAR Fields for Novel View Synthesis2 May 2023
- 2023Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data24 Feb 2023
- 2022Synthetic Point Cloud Generation for Class Segmentation Applications7 May 2022