Terrestrial Laser Scanning Point Clouds Research Articles

Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.

Target-based georeferencing of terrestrial radar images using TLS point clouds and multi-modal corner reflectors in geomonitoring applications

Terrestrial Radar Interferometry (TRI) is widely adopted in geomonitoring applications due to its capability to precisely observe surface displacements along the line of sight, among other key characteristics. As its deployment grows, TRI is also increasingly used to monitor smaller and more dispersed geological phenomena, where the challenge is their precise localization in 3d space if the pose of the radar interferometer is not known beforehand. To tackle this challenge, we introduce a semi-automatic target-based georeferencing method for precisely aligning TRI data with 3d point clouds obtained using long-range Terrestrial Laser Scanning (TLS). To facilitate this, we developed a multi-modal corner reflector (mmCR) that serves as a common reference point recognizable by both technologies, and we accompanied it with a semi-automatic data-processing pipeline, including the algorithms for precise center estimation. Experimental validation demonstrated that the corner reflector can be localized within the TLS data with a precision of 3-5 cm and within the TRI data with 1-2 dm. The targets were deployed in a realistic geomonitoring scenario to evaluate the implemented workflow and the achievable quality of georeferencing. The post-georeferencing mapping uncertainty was found to be on a decimeter level, matching the state-of-the-art results using dedicated targets and achieving more than an order of magnitude lower uncertainty than the existing data-driven approaches. In contrast to the existing target-based approaches, our results were achieved without laborious visual data inspection and manual target detection and on significantly larger distances, surpassing 2 kilometers. The use of the developed mmCR and its associated data-processing pipeline extends beyond precise georeferencing of TRI imagery to TLS point clouds, allowing for alternatively georeferencing using total stations, mapping quality evaluation as well as on-site testing and calibrating TRI systems within the application environment.

Reconciling short- and long-term measurements of coastal cliff erosion rates

Oblique terrestrial laser scanning (TLS) enables topographic change detection at scales (10−1 –100 cm) that are appropriate for coastal cliff erosion monitoring. Despite this, published applications of TLS on cliff are limited to a small number of sites around the world. Here we report new TLS point cloud datasets from 9 years of monitoring (2014–2023) at Rothesay Bay within the Hauraki Gulf, New Zealand, which has relatively low wave energy and a meso-tidal range. The 120 m-length scan area includes cliffs of 10–30 m height, formed of horizontally bedded soft sedimentary flysch rock. The cliffs are fronted by a 140 m wide near-horizontal shore platform that terminates in an abrupt seaward edge. Previous research at this site has estimated long-term cliff erosion rates within a range of 1.4 ± 0.1 to 14.3 ± 0.1 mm/year on the basis of measured shore platform width, assuming that the shore platform has widened over time over 6000 years of stable Holocene sea level, and that the seaward edge of the shore platform has not retreated. Volumetric cliff-face erosion rates were detected through 15 scans over a 9-year window (2014–2023), including intensive monthly TLS scanning between July 2021 and July 2022. Results show that the average cliff recession rate over the past decade has been 41 ± 2 mm/year, and monthly scans show a range in erosion rates of 30 to 288 mm/year. The cliff recession rate detected with TLS is 3 to 30 times higher than erosion rates derived based on the shore platform width. If erosion had been constant at this rate over approximately 6000 years, a total cliff retreat of >245 m would be expected, whereas the contemporary shore platform is only 140 m wide. We discuss two possible hypotheses for the confounding width of the modern shore platform: 1) that modern cliff retreat rates are faster than past erosion rates; 2) that the seaward edge of the shore platform does not reliably demarcate Holocene cliff recession. We present new bathymetric survey mapping seaward of the shore platform edge that reveals multiple rocky features that are distinguished by steep slope breaks and planar surfaces. Understanding the evolution of the intertidal shore platforms during the Holocene era may necessitate new insights on how cliffs formed toward the end of the last marine transgression. This could potentially be investigated through the study of subtidal marine bathymetry.

Digital reconstruction of railway steep slope from UAV+TLS using geometric transformer

Accurate representation of railway slopes, especially those that are steep, is vital for real-time risk perception. Also, temporary structures also present certain safety hazards due to lack of monitoring. Traditional point cloud modeling, employing Unmanned Aerial Vehicle (UAV) or Terrestrial Laser Scanning (TLS), often struggles to simultaneously account for the precision of both surface and overhead models, leading to considerable model distortion, roughness, and deviation. Addressing these issues, A new 3D point cloud modeling algorithm for railway slopes based on a geometric transformer is presented in this paper. This involves an innovative rough point cloud denoising technique leveraging adaptive segmentation, multi-scale denoising, and deep learning point cloud registration. Our approach significantly enhances UAV point cloud accuracy and supplements missing portions of the TLS point cloud dataset occluded by objects block, using data from the UAV point cloud set. An experimental study shows that the score-based denoising algorithm improves precision from 37.44 mm to 8.11 mm for a UAV 3D point cloud. Further, by registering the UAV and TLS point cloud sets using the Geometric Transformer algorithm, the precision of the 3D point cloud was further augmented to 5.11 mm, representing a sevenfold enhancement over the initial UAV point cloud accuracy prior to denoising. Consequently, a high-fidelity 3D point cloud model of steep railway slopes has been created.

Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation

Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.

A framework for automated reconstruction of communication towers from terrestrial laser scanning point clouds

ABSTRACT Communication towers provide reliable wireless communication services and play a vital role in modern society. This paper presents an automatic 3D reconstruction framework for communication towers based on point clouds. The antenna is reconstructed by a model-driven method, which constructs an energy function including the data and smoothing terms to evaluate the model accuracy. The parameters of the antenna model are obtained by minimizing energy function with hybrid Genetic Algorithm (GA) and Simulated Annealing (SA). And the predefined model is transformed using these parameters to achieve antenna 3D reconstruction. For tower body reconstruction, the point cloud of the tower body is finely extracted with the region growing followed by piecewise fitting to obtain the model. The performance of the proposed method is evaluated on point clouds from six scenes. The average root-mean-square error (RMSE) of antenna reconstruction is 1.3 cm. The average precision, recall, and F1-score values of the tower body point extraction are 99.9%, 93.5%, and 96.6%, respectively. The average RMSE of tower body reconstruction is 1.2 cm. The experimental results indicate that the method can effectively reconstruct the antenna and tower body with point clouds, providing a feasible framework for the 3D reconstruction of communication towers.

Automatic detection of color markings and numbers on trees in point clouds from Personal Laser Scanning (PLS) and Terrestrial Laser Scanning (TLS)

In forestry, colored spray paint is used for different purposes, such as marking trees that are chosen for the next harvest or that will be promoted by future silvicultural activities, or for the demarcation of skid/cable roads. Numerous studies have demonstrated the application of Terrestrial Laser Scanners (TLS) and Personal Laser Scanners (PLS) for the acquisition of tree parameters (diameters, heights, and tree positions) in a forest inventory context. However, no studies have included classification based on spectral information by applying colored markings to trees. When a laser scanner is combined with a camera, the color information can be mapped onto the laser point cloud, allowing for additional analysis of spectral information. In this study, we have tested the analysis of joint information from imagery and 3D point clouds in two different settings: (i) PLS point cloud data from a GeoSLAM ZEB Horizon were complemented with spectral information from images collected with a NCTech iSTAR Pulsar 360-degree camera, (ii) TLS point cloud data from a RIEGL VZ-400i were combined with imagery from a Nikon D850 digital single lens reflex (DSLR) camera with 14 mm fisheye lens. The major objective was to automatically classify trees that were marked with colored sprays automatically identify the numbers sprayed on the trees. The marked trees could be classified correctly with a balanced accuracy of 77.26% for PLS and 98.01% for the data collected with the TLS system using a support vector machine (SVM) model. The numbers written on the trees were classified from the TLS data using a YOLOv8 model. The accuracy of the digit classification reached up to 97.30%. In conclusion, it is possible to automatically detect marked trees and to automatically recognize the numbers spayed on these trees from colored point cloud data. However, accuracy strongly depends on the selected combination of scanner and camera. The findings of future research on this subject might differ with the ongoing development of PLS systems and could be enhanced by creating a large dataset of handwritten numbers collected using laser scanning systems.

An Individual Tree Detection and Segmentation Method from TLS and MLS Point Clouds Based on Improved Seed Points

Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this study, we developed a bottom–up framework for ITDS based on seed points. The proposed method is based on density-based spatial clustering of applications with noise (DBSCAN) to initially detect the trunks and filter the clusters by a set threshold. Then, the K-Nearest Neighbor (KNN) algorithm is used to reclassify the non-core clustered point cloud after threshold filtering. Furthermore, the Random Sample Consensus (RANSAC) cylinder fitting algorithm is used to correct the trunk detection results. Finally, we calculate the centroid of the trunk point clouds as seed points to achieve individual tree segmentation (ITS). In this paper, we use terrestrial laser scanning (TLS) data from natural forests in Germany and mobile laser scanning (MLS) data from planted forests in China to explore the effects of seed points on the accuracy of ITS methods; we then evaluate the efficiency of the method from three aspects: trunk detection, overall segmentation and small tree segmentation. We show the following: (1) the proposed method addresses the issues of missing segmentation and misrecognition of DBSCAN in trunk detection. Compared to using DBSCAN directly, recall (r), precision (p), and F-score (F) increased by 6.0%, 6.5%, and 0.07, respectively; (2) seed points significantly improved the accuracy of ITS methods; (3) the proposed ITDS framework achieved overall r, p, and F of 95.2%, 97.4%, and 0.96, respectively. This work demonstrates excellent accuracy in high-density forests and is able to accurately segment small trees under tall trees.

Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data.

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.

The Legacy of Sycamore Gap: The Potential of Photogrammetric AI for Reverse Engineering Lost Heritage with Crowdsourced Data

Abstract. The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian's Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage.

Development of a Precise Tree Structure from LiDAR Point Clouds

Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.

Treegraph: tree architecture from terrestrial laser scanning point clouds

AbstractAccurate quantification of tree architecture is critical to interpreting the growth, health and functioning of trees and forests. Terrestrial laser scanning (TLS) offers millimetre‐level point cloud data, but current approaches to 3D tree reconstruction from TLS point clouds primarily focus on retrieving total volume at tree scale for aboveground biomass (AGB) estimation. Few methods have been designed specifically to provide tree architectural properties, including branch‐level morphology and topology, rather than AGB; derived topological traits have tended to be a compromise, and of secondary importance to volume. We present Treegraph, a new approach explicitly designed to retrieve the architectural traits of trees at multiple scales, from the whole tree scale down to individual branches and internodes, using TLS data with limited assumptions about tree form. It provides morphological traits such as branch length and diameter, alongside topological traits including parent–daughter connections of branches and internodes, furcation (branching) number and branch order. We compare Treegraph‐derived morphological and topological traits with manual measurements of branches from eight destructively harvested trees, yielding RMSE values of 0.60 m (5.96%) for branch length, 2.99 cm (33.45%) for branch diameter, 0.46 (19.38%) for furcation number and 0.08 m (33.16%) for internode length, respectively. In a broader application to 603 trees from tropical, temperate and urban forests, we demonstrate that the derived morphological and topological traits support testing of structure‐related metabolic scaling theories. Testing branches over 10 cm in diameter across 18 657 branching nodes shows that Treegraph‐derived branch‐level scaling exponents deviate from WBE predictions, exhibiting area‐preserving behaviour while displaying asymmetry in length and diameter of daughter branches. Available as open‐source Python software, Treegraph provides fine‐level branching network information, promoting improved insights into tree structure and function. This data‐driven approach reduces the need for empirical heuristic parameters, which has the potential for advancing large‐scale ecological studies on tree architecture.

Evaluation of 2D affine — hand-crafted detectors for feature-based TLS point cloud registration

Abstract The development of modern surveying methods, particularly, Terrestrial Laser Scanning (TLS), has found wide application in protecting and monitoring engineering and objects and sites of cultural heritage. For this reason, it is crucial that several factors a˛ecting the correctness of point cloud registration are considered, including the correctness of the distribution of control points (both signalised and natural), the quality of the process, and robustness analysis. The aim of this article is to evaluate the quality and correctness of TLS registration based on point clouds converted to raster form (in spherical mapping) and hand-crafted detectors. The expanded Structure-from-Motion (SfM) was used to detect the tie points for TLS registration and reliability assessment. The results demonstrated that affine detectors are useful in detecting a high number of key points (increased for point detectors by 8–12 times and for blob detectors by about 10–24 times), improving the quality and TLS registration completeness. For the registration accuracy of point cloud on signalised check points, the lower values can be noted for maximum RMSE errors for blob affine detectors than detectors and larger values for corner detectors and affine detectors (not more than 4 mm in the extreme cases, typically 2 mm). The commonly-applied target-based registration method yields similar results (di˛erences do not exceed – in extreme cases – 3.5 mm, typically less than 2 mm), proving that using affine detectors in the TLS registration process is and reasonable and can be recommended.

Assessment of Panorama Photogrammetry as a Tool for Long-Range Deformation Monitoring.

This study investigates panorama photogrammetry (PPh) as a potential method to collect massive 3D information for long-range deformation monitoring. Particularly, this study focuses on areas with measuring restrictions, i.e., inaccessible objects and distances above 0.6 km. Under these particular conditions, geodetic techniques based on Electromagnetic Distance Meters (EDMs) or Total Stations (TSs) can provide coordinates with a precision better than 1 cm, but only for a limited number of discrete points. For mass capture, Terrestrial Laser Scanning (TLS) is normally the preferred solution, but long-range instruments are expensive, and drawbacks such as weak return signals and non-automatic target recognition appear. As an alternative, PPh is investigated in the well-controlled area of La Muela in Cortes de Pallas, where images are automatically captured from geodetic pillars using a GigaPan device, processed, and then rigorously compared to TLS point clouds. The results obtained after integrating both techniques into a high-accuracy geodetic reference frame show that PPh and TLS provide similar precision to within approximately 4 cm in the range of 0.6-1.0 km. Therefore, considering cost-effectiveness and ease of use, the proposed method can be considered a low-cost alternative to TLS for long-range deformation monitoring.

Heritage Building Information Modelling (HBIM): A Review of Published Case Studies

Abstract. This paper presents a systematic review of published case studies on Heritage Building Information Modelling (HBIM) since 2018, and identifies research gaps in the subject matter. Building upon the foundational work of Ewart and Zuecco (2019), this research aims to reveal the latest trends in HBIM implementation, identify recent developments of HBIM technologies, changes in the purpose of HBIM programs and stakeholder roles and responsibilities, and uncover knowledge gaps that provide avenues for future research. Utilizing the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach, two major academic databases, Scopus and Web of Science (WOS), were searched, resulting in a rich and diverse dataset for analysis. The paper reports findings on the status of reality capture techniques used to acquire data for HBIM development, focusing on terrestrial laser scanning (TLS) technology. The review highlights the benefits and limitations of TLS for data acquisition in HBIM, as well as the integration of TLS with other reality capture technologies, such as Structure from Motion (SfM) and photogrammetry. The paper further outlines the typical workflow for processing TLS scan data and explores the integration of multiple point clouds for comprehensive heritage site modeling. In addition to the state of the art, this systematic review also uncovers several research gaps in the field of HBIM that offer opportunities for future research and innovation, including the lack of guidelines for data acquisition in HBIM programs, the predominantly manual development process of HBIM from TLS point cloud data, and the under-utilized capacity of TLS for long-term monitoring and change detection. This comprehensive review provides valuable insights into the current landscape of HBIM, offering guidance for future research and development in the heritage sector and highlighting areas in need of further investigation to advance the field.

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Multi-source point cloud registration for urban areas using a coarse-to-fine approach

ABSTRACT The foundational step in constructing a digital twin involves transforming the physical world into an immersive 3D virtual environment. Unmanned aerial vehicle (UAV)-based photogrammetry, mobile mapping system (MMS), and terrestrial laser scanning (TLS) serve as powerful tools for 3D reconstruction of urban environments. Given the distinctive strengths and weaknesses of each sensor, a multi-source point cloud registration technique is imperative to achieve a detailed and precisely geolocated 3D urban model. Hence, we developed coarse-to-fine approach methods, called building exterior wall-based (BEWB) and building outline-based (BOB) algorithms, to register point clouds captured by diverse sensors in urban scenes. The BEWB algorithm coarsely registers urban point clouds by extracting building exterior walls, establishing correspondence points, and effectively removing outliers within this correspondence point set. The BOB algorithm precisely registers the urban point clouds acquired from multiple sensors by leveraging building outlines and points corresponding to the ground of the point clouds. To validate the proposed algorithms, we initially registered a UAV-based photogrammetry point cloud with the MMS point cloud and subsequently registered the TLS point clouds. Comprehensive quantitative and qualitative analyzes of the results demonstrate that our algorithms outperform existing methods in achieving precise registration. By successfully registering three distinct point clouds, we generated a comprehensive urban scene point cloud characterized by enhanced precision, point density, and geolocation accuracy.

Indoor view-based visibility analysis using UAV and TLS point clouds with Line-of-Sight correction

Visibility analysis is a crucial geographic information processing method, used to assess the observable spatial range from a particular location under specific conditions. It has broad applications, such as urban planning, landscape design and environmental research. Laser scanning provides detailed and accurate 3D data for visibility analysis, called point cloud. However, certain deficiencies still exist in current point cloud-based visibility analysis methods. Few methods are developed from indoor perspective and the influence of point cloud void is rarely considered. Firstly, indoor viewpoints are generated based on the windows extracted from terrestrial laser scanning (TLS) point cloud and unmanned aerial vehicle (UAV) point cloud is used to provide 3D representation of the environment around the viewpoint. Then, a half-sphere viewshed is created for each indoor viewpoint to analyze the visible spatial range. In this step, a novel line-of-sight correction method is proposed based on the semantic information from UAV point cloud, to correct the biased observation distance resulted from point cloud voids. Lastly, the volume of visible space is calculated based on the corrected viewshed image, to evaluate the visibility of each indoor viewpoint. Our method is validated on two datasets. The results indicate that our method enables more accurate simulation of visible space compared with previous studies by considering the effect of point cloud void. The proposed method can support the urban design from the perspective of indoor visual perception and accurate calculation of other visual factors in visual quality evaluation.

Digital 3D Reconstruction of Ancient Chinese Great Wild Goose Pagoda by TLS Point Cloud Hierarchical Registration

Digital reconstruction of ancient buildings is very challenging due to its architectural complexity and structural delicacy. Therefore, how to apply new Earth Observation (EO) technology to digitally reconstruct the complete and real model of ancient buildings without damage has become an urgent issue. This article proposes a multi-scan point cloud hierarchical registration method for 3D reconstruction of the ancient Chinese Great Wild Goose Pagoda based on Terrestrial Laser Scanner (TLS) data. First, a multi-scan point cloud hierarchical registration method is elaborately designed for 3D reconstruction. Then, the outline feature of the ancient Chinese Great Wild Goose Pagoda is automatically extracted by point cloud slicing. Finally, fine modeling is carried out according to the outline feature extracted from the reconstructed point cloud, and after that block combination and texture mapping are conducted for 3D digitalization and documentation. The experiments indicate that the Root Mean Square Error (RMSE) of multi-scan hierarchical registration of the translation and rotation are less than 3 cm and 0.862 ∘ , respectively, and the complete and real model of the Great Wild Goose Pagoda is obtained by fine modeling and texture mapping, which provides an important reference for 3D digital protection of ancient buildings.

Efficient shrub modelling based on terrestrial laser scanning (TLS) point clouds

ABSTRACT This paper presents a methodology for automatically generating 3D models of individual shrubs from point clouds acquired by Terrestrial Laser Scanning (TLS) devices. The creation of accurate 3D models for shrubs holds significant value for ecological studies. The shrub modelling is more challenging compared to other tree modelling tasks, due to the presence of substantial noise and fuzzy branch structures. To address these challenges, our approach first partitions the input point cloud into two categories: branches and others. This point segmentation process effectively eliminates interference of leaves and noise, thereby enhancing the visibility of branch skeletons. Then, we construct a triangulation network using the branch points and establish a minimum spanning tree (MST) based on this network. Serving as the initial skeleton representation of the plant, the MST preserves the essential topological structures of the branches. Within the extracted MST, we implement a recursive trimming technique to eliminate redundant branches by merging adjacent points and edges, ultimately consolidating the skeleton structure. Finally, we employ an adaptive cylinder fitting algorithm that relies on robust principal component analysis (RPCA) to generate the model. The effectiveness and robustness of the proposed method are demonstrated via experiments on different datasets, with an average fitting error of 1.13 cm, indicating that our approach can achieve high accuracy in modelling individual shrubs.