A Rasterizing Massive LiDAR Points Cloud Algorithm Based on Triangle Driver

Individual tree detection from airborne laser scanning data based on supervoxels and local convexity

Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overlaps in the physical space, and thus spatially encoded data can be (re-)used to encode the temporal stream. Temporal encoding by reusing spatial encoding data not only improves the compression rate, but also avoids redundant computations, which significantly improves the compression speed. Experiments show that our compression system achieves 40× to 90× compression rate, significantly higher than the MPEG’s LiDAR point cloud compression standard, while retaining high end-to-end application accuracies. Meanwhile, our compression system has a compression speed that matches the point cloud generation rate by today LiDARs and out-performs existing compression systems, enabling real-time point cloud transmission.

Leveraging the power of multi-core platforms for large-scale geospatial data processing: Exemplified by generating DEM from massive LiDAR point clouds

Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees.

Processing the massive LiDAR point cloud is a time consuming process due to the magnitude of the data involved and the highly computational iterative nature of the algorithms. In particular, many current and future applications of LiDAR require real- or near-real-time processing capabilities. Relevant examples include environmental studies, military applications, tracking and monitoring of hazards. Recent advances in Graphics Processing Units (GPUs) open a new era of General-Purpose Processing on Graphics Processing Units (GPGPU). In this paper, we seek to harness the computing power available on contemporary Graphic Processing Units (GPUs), to accelerate the processing of massive LiDAR point cloud. We propose a CUDA-based method capable of accelerating processing of massive LiDAR point cloud on the CUDA-enabled GPU. Our experimental results showed that we are able to significantly reduce processing time of constructing TIN from LiDAR point cloud with GPGPU based parallel processing implementation, in comparison with the current state-of-the-art CPU-based algorithms.

Abstract. Training LiDAR point clouds object detectors requires a significant amount of annotated data, which is time-consuming and effort-demanding. Although weakly supervised 3D LiDAR-based methods have been proposed to reduce the annotation cost, their performance could be further improved. In this work, we propose a weakly supervised LiDAR-based point clouds vehicle detector that does not require any labels for the proposal generation stage and needs only a few labels for the refinement stage. It comprises two primary modules. The first is an unsupervised proposal generation module based on the geometry of point clouds. The second is the pseudo-label refinement module. We validate our method on two point clouds based object detection datasets, namely KITTI and ONCE, and compare it with various existing weakly supervised point clouds object detection methods. The experimental results demonstrate the method’s effectiveness with a small amount of labeled LiDAR point clouds.

This paper provides a practical procedure for fusing LiDAR and photogrammetric point clouds for the extraction of tree metrics. Aerial photogrammetric point clouds are first generated using the structure-from-motion and dense-matching methods. Registration of the LiDAR and photogrammetric point clouds is then performed using an onboard global positioning system and inertial measurement unit. However, due to systematic deviations, it is impossible to directly merge the two types of point cloud. Therefore, an urban street network obtained from the OpenStreetMap digital mapping system is utilized for point cloud segmentation. After segmentation, each chunk is finely registered and merged based on the iterative closest point algorithm, allowing the two types of data to be accurately co-registered and a fused point cloud obtained. Finally, we conducted experiments to extract stand and individual tree metrics from fused point clouds created for two study plots. The height distributions of the fused point clouds were highly consistent with LiDAR data, with the 5%, 10%, 25%, 50%, 75%, 90%, and 95% height percentiles showing acceptable similarities. The height distribution of individual trees was also consistent with that of field measurements. Furthermore, the fused point clouds contain a high point density and RGB color information, which allow shape delineation and estimation of tree health status. This comprehensive analysis demonstrates that this procedure provides a practical way to inventory tree stands and individuals in urban areas.

In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final versions translate into a high demand for disk space and therefore electricity. It is therefore obvious that in order to reduce energy consumption, lower the carbon footprint of the activity and sensitize sustainability in the digitization of the industry, lossless compression of the aforementioned datasets is a good solution. In this article, a new format for point clouds—3DL—is presented, the effectiveness of which is compared with 21 available formats that can contain LiDAR data. A total of 404 processes were carried out to validate the 3DL file format. The validation was based on four LiDAR point clouds stored in LAS files: two files derived from ALS (airborne laser scanning), one in the local coordinate system and the other in PL-2000; and two obtained by TLS (terrestrial laser scanning), also with the same georeferencing (local and national PL-2000). During research, each LAS file was saved 101 different ways in 22 different formats, and the results were then compared in several ways (according to the coordinate system, ALS and TLS data, both types of data within a single coordinate system and the time of processing). The validated solution (3DL) achieved CR (compression rate) results of around 32% for ALS data and around 42% for TLS data, while the best solutions reached 15% for ALS and 34% for TLS. On the other hand, the worst method compressed the file up to 424.92% (ALS_PL2000). This significant reduction in file size contributes to a significant reduction in energy consumption during the storage of LiDAR point clouds, their transmission over the internet and/or during copy/transfer. For all solutions, rankings were developed according to CR and CT (compression time) parameters.

LiDAR point cloud is essential for autonomous vehicles, but motion distortions from dynamic objects degrade the data quality. While previous work has considered distortions caused by ego motion, distortions caused by other moving objects remain largely overlooked, leading to errors in object shape and position. This distortion is particularly pronounced in high-speed environments such as highways and in multi-LiDAR configurations, a common setup for heavy vehicles. To address this challenge, we introduce HiMo, a pipeline that repurposes scene flow estimation for non-ego motion compensation, correcting the representation of dynamic objects in point clouds. During the development of HiMo, we observed that existing self-supervised scene flow estimators often produce degenerate or inconsistent estimates under high-speed distortion. We further propose SeFlow++, a real-time scene flow estimator that achieves state-of-the-art performance on both scene flow and motion compensation. Since well-established motion distortion metrics are absent in the literature, we introduce two evaluation metrics: compensation accuracy at a point level and shape similarity of objects. We validate HiMo through extensive experiments on Argoverse 2, ZOD and a newly collected real-world dataset featuring highway driving and multi-LiDAR-equipped heavy vehicles. Our findings show that HiMo improves the geometric consistency and visual fidelity of dynamic objects in LiDAR point clouds, benefiting downstream tasks such as semantic segmentation and 3D detection. See https://kin-zhang.github.io/HiMo for more details.

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation.

Abstract. This study aims to detect automatically building points: (a) from LIDAR point cloud using simple techniques of filtering that enhance the geometric properties of each point, and (b) from a point cloud which is extracted applying dense image matching at high resolution colour-infrared (CIR) digital aerial imagery using the stereo method semi-global matching (SGM). At first step, the removal of the vegetation is carried out. At the LIDAR point cloud, two different methods are implemented and evaluated using initially the normals and the roughness values afterwards: (1) the proposed scan line smooth filtering and a thresholding process, and (2) a bilateral filtering and a thresholding process. For the case of the CIR point cloud, a variation of the normalized differential vegetation index (NDVI) is computed for the same purpose. Afterwards, the bare-earth is extracted using a morphological operator and removed from the rest scene so as to maintain the buildings points. The results of the extracted buildings applying each approach at an urban area in northern Greece are evaluated using an existing orthoimage as reference; also, the results are compared with the corresponding classified buildings extracted from two commercial software. Finally, in order to verify the utility and functionality of the extracted buildings points that achieved the best accuracy, the 3D models in terms of Level of Detail 1 (LoD 1) and a 3D building change detection process are indicatively performed on a sub-region of the overall scene.

Abstract. Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the reliability of the ceilings towards indoor localisation, by using components that are included in a simple mobile device. The choice of ceilings lies in their advantages, which include the incorporation of various characteristic components, as well as the absence of obstacles between them and the sensor. Indoor localisation is achieved based on LiDAR point clouds and images from RGB sensors of mobile devices. Additionally, this research involves location tracking of different users, to discover different movement patterns in an indoor facility. The proposed methodology revealed the robustness of the Coloured ICP algorithm for in-door localisation based on point clouds, both in terms of time efficiency and quality, while the combination of the SURF feature detector and SIFT descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergencies, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations. The captured point clouds of the ceilings can also be used as a reference to CAD and BIM models, to help the modelling of the existing utilities and their components in an indoor facility.

Under low-light nighttime conditions, building images often suffer from poor quality, making high-precision integration with point cloud data challenging. This paper presents a comprehensive solution for enhancing nighttime building images and fusing them with point clouds for modeling. We developed an unsupervised learning framework that in-corporates prior knowledge of building structures. Based on RetinexDIP theory, the framework includes a structural edge enhancement module. It improves the structure of initial lighting and refines it with edge-aware adjustments using reflectance information, significantly enhancing the clarity and detail of building facades and outlines. Experi-mental results show that this method significantly improves image quality on both the MIT-Adobe FiveK dataset and a self-collected dataset. The average PSNR reached 12.02 dB, which is 0.35 dB higher than baseline methods, and the information entropy increased by 0.08. Moreover, it effectively preserves the integrity of building edge structures. We also proposed a Line-Feature-Constrained Registration (LFR) method for fusing image-based point clouds and LiDAR point clouds. This approach identifies building outline feature point clouds, extracts corresponding lines, and decomposes 3D registration into planar and elevation registration. In three test areas, the LFR method achieved a registration ac-curacy (RMSE) of 0.04-0.06 meters and an overlap rate of approximately 95%, showing significant improvements in accuracy and robustness over traditional ICP and NDT methods. Finally, we performed fusion modeling using the enhanced images and regis-tered point clouds. The reconstructed building models outperform comparative methods in both geometric completeness and texture authenticity. The number of tie points in-creased by up to 27.6%, while the median errors in plane and elevation were reduced to 4.15 cm and 5.53 cm, respectively.

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

Both UAV photogrammetry and lidar have become common in deriv- ing three-dimensional models of urban scenes, and each has its own advantages and disadvantages. However, the fusion of these multi- source data is still challenging, in which registration is one of the most important stages. In this paper, we propose a method of coarse point cloud registration which consists of two steps. The first step is to extract urban building facades in both an oblique photogram- metric point cloud and a lidar point cloud. The second step is to align the two point clouds using the extracted building facades. Object Vicinity Distribution Feature (Dijkman and Van Den Heuvel 2002) is introduced to describe the distribution of building facades and register the two heterologous point clouds. This method provides a good initial state for later refined registration process and is transla - tion, rotation, and scale invariant. Experiment results show that the accuracy of this proposed automatic registration method is equiva- lent to the accuracy of manual registration with control points.