Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++

Pena, Alberto Manuel Esmorís; Yermo, Miguel; Weiser, Hannah; Winiwarter, Lukas; Höfle, Bernhard; Rivera, Francisco F.

doi:10.1109/access.2022.3211072

Cited by 6 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantage of the ray-tracing method [6][7][8] lies in its ability to accurately simulate the propagation and scattering process of light, as well as generate high-precision ground object echo signals. However, when dealing with complex scenes, the calculation load of ray tracing significantly increases due to multiple reflections of light, resulting in low simulation efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, the corresponding reflected angle (θ r ) is obtained. By utilizing Equation (8), the value of f sur (θ i , θ r ) is calculated, which represents the reflectance function for light energy. Finally, substituting f sur (θ i , θ r ) into Equation (4) allows us to determine Q out , representing the reflected light energy.…”

Section: Surface Light Energy Reflectance Modelmentioning

confidence: 99%

“…The main methods for simulating lidar target echoes include ray tracing [6][7][8], physical optics simulation [9][10][11], radar scattering field simulation [7,12,13], and other approaches. The ray-tracing method is capable of accurately simulating the propagation and scattering process of light, thereby generating high-precision echo signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Ground Object Echo Simulation of Avian Lidar

Su,

Sang,

Hao

et al. 2024

Photonics

View full text Add to dashboard Cite

The clutter suppression effect of ground objects significantly impacts the detection and tracking performance of avian lidar on low-altitude bird flock targets. It is imperative to simulate the point cloud data of ground objects in lidar to explore effective methods for suppressing clutter caused by ground objects in avian lidar. The traditional ray-tracing method is enhanced in this paper to efficiently obtain the point cloud simulation results of ground objects. By incorporating a beam constraint and a light-energy constraint, the screening efficiency of effective rays is improved, making them more suitable for simulating large scenes with narrow lidar beams. In this paper, a collision detection scheme is proposed based on beam constraints, aiming to significantly enhance the efficiency of ray-tracing collision detection. The simulation and experimental results demonstrate that, in comparison with other conventional simulation methods, the proposed method yields the point cloud results of ground objects that exhibit greater conformity to the actual lidar-collected point cloud results in terms of shape characteristics and intensity features. Additionally, the simulation speed is significantly enhanced.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Surface Light Energy Reflectance Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Ground Object Echo Simulation of Avian Lidar

Su,

Sang,

Hao

et al. 2024

Photonics

View full text Add to dashboard Cite

show abstract

“…It is suggested that LiDAR simulators may benefit from high-performance computing (HPC) algorithms or techniques. For instance, using HELIOS++ as a case study, novel algorithms run on parallel computers boost the performance [37]. It is worth further investigating how the LESS LiDAR simulator can benefit from these algorithms or techniques.…”

Section: Discussionmentioning

confidence: 99%

LESS LiDAR: A Full-Waveform and Discrete-Return Multispectral LiDAR Simulator Based on Ray Tracing Algorithm

Luo,

Xie,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984. Their mean was 0.9698. The number of discrete returns fitted well and the coefficient of determination was 0.9986. A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849. The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals.

show abstract

“…Besides, it supports detailed XML specifications for different platforms and scanners. Instead of a ray tracing implementation similar to the native ray tracing of computer games such as Grand Theft Auto V (GTA V) and 3D graphics software such as Blender, HELIOS++ provides an efficient object-aware ray tracing algorithm that supports the high-performance parallel computation of different physical models on the light ray (Esmorís et al, 2022).…”

Section: Virtual Laser Scanningmentioning

confidence: 99%

Deep learning with simulated laser scanning data for 3D point cloud classification

Esmorís Pena,

Weiser,

Winiwarter

et al. 2024

Preprint

View full text Add to dashboard Cite

Laser scanning is an active remote sensing technique applied in many disciplines to acquire state-of-the-art spatial measurements. Semantic labeling is often necessary to extract information from the raw point cloud. Deep learning methods constitute a data-hungry solution for the semantic segmentation of point clouds. In this work, we investigate the use of simulated laser scanning for training deep learning models, which are applied to real data subsequently. We show that training a deep learning model purely on virtual laser scanning data can produce results comparable to models trained on real data when evaluated on real data. For leaf-wood segmentation of trees, using the KPConv model trained with virtual data achieves 93.7% overall accuracy, while the model trained on real data reaches 94.7% overall accuracy. In urban contexts, a KPConv model trained on virtual data achieves 74.1% overall accuracy on real validation data, while the model trained on real data achieves 82.4%. Our models outperform the state-of-the-art model FSCT in terms of generalization to unseen real data as well as a baseline model trained on points randomly sampled from the tree mesh surface. From our results, we conclude that the combination of laser scanning simulation and deep learning is a cost-effective alternative to real data acquisition and manual labeling in the domain of geospatial point cloud analysis. The strengths of this approach are that a) a large amount of diverse laser scanning training data can be generated quickly and without the need for expensive equipment, b) the simulation configurations can be adapted so that the virtual training data have similar characteristics to the targeted real data, and c) the whole workflow can be automated through procedural scene generation.

show abstract

Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++

Abstract: Performance measurements on the FinisTerrae-II supercomputer were possible thanks to the CESGA (Galician supercomputing center).

Cited by 6 publications

References 35 publications

Research on Ground Object Echo Simulation of Avian Lidar

Research on Ground Object Echo Simulation of Avian Lidar

LESS LiDAR: A Full-Waveform and Discrete-Return Multispectral LiDAR Simulator Based on Ray Tracing Algorithm

Deep learning with simulated laser scanning data for 3D point cloud classification

Contact Info

Product

Resources

About