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 met: (i) models of 3D scene and scanner are available and (ii) modelling of the beam-scene interaction is simplified to a computationally feasible while physically realistic level. 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. Features of HELIOS++ include the availability of Python bindings (pyhelios) for controlling simulations, and a range of model types for 3D scene representation. Such model types include meshes, digital terrain models, point clouds and partially transmissive voxels, which are especially useful in laser scanning simulations of vegetation. In a scene, object models of different types can be combined, so that representations spanning multiple spatial scales in different resolutions and levels of detail are possible. HELIOS++ follows a modular design, where the core components of platform, scene, and scanner can be individually interchanged, and easily configured. 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. We show how HELIOS++ positions among other VLS software in terms of input model support and simulation of beam divergence in a literature survey. We also perform a direct comparison of simulations with DART, where we employ a scene from the Radiative Transfer Model Intercomparison (RAMI). This example shows that HELIOS++ takes about 10 times longer than DART for parsing and preparing the 3D scene, but performs about 314,000 times faster in the beam simulation, achieving 200,000 rays/s. Comparing HELIOS++ to its predecessor, HELIOS, revealed reduced runtimes by up to 99%. Virtually scanned point clouds may be used for a broad range of applications as shown in literature. We could identify four main categories of use cases prevailing at present, which benefit from simulated LiDAR point clouds: data acquisition planning, method evaluation, method training and sensing experimentation. We conclude that a general-purpose LiDAR simulator can be employed for many different scientific applications, as long as it is ensured that the simulation adequately represents reality, which is specific to the given research question.
Keywords: Software;LiDAR simulation;Point cloud;Data generation;Voxel;Vegetation modelling