Search

This article describes procedures and thoughts regarding the reconstruction of geometry-given data and its uncertainty. The data are considered as a continuous fuzzy point cloud, instead of a discrete point cloud. Shape fitting is commonly performed by minimizing the discrete Euclidean distance; however, we propose the novel approach of using the expected Mahalanobis distance. The primary benefit is that it takes both the different magnitude and orientation of uncertainty for each data point into account. We illustrate the approach with laser scanning data of a cylinder and compare its performance with that of the conventional least squares method with and without random sample consensus (RANSAC). Our proposed method fits the geometry more accurately, albeit generally with greater uncertainty, and shows promise for geometry reconstruction with laser-scanned data. [ABSTRACT FROM AUTHOR]

Introduction: Dobšiná Ice Cave (Slovakia) has attracted the attention of many researchers since its discovery more than 150 years ago. Although the cave is located outside the high-mountain area, it hosts one of the largest volumes of underground perennial ice. The topographic mapping of this unique UNESCO Natural Heritage site has led to several historical surveys. In the last decades of rapid climate change, this natural formation has been subject to rapid changes that are dynamically affecting the shape of the ice body. Increased precipitation, the rise in year-round surface temperatures, and the gravity cause significant shape changes in the ice filling. Methods: This paper describes modern technological tools to comprehensively survey and evaluate interannual changes in both the floor and wall of the underground ice body. Technologies such as digital photogrammetry, in combination with precise digital tacheometry and terrestrial laser scanning, make it possible to detect ice accumulation and loss, including the effect of sublimation due to airflow, as well as sliding movements of the ice body to the lower part of the cave. To get a comprehensive model of the ice volume, geophysical methods (microgravimetry and ground penetrating radar) have been added to determine the thickness of the floor ice in the upper parts of the cave in the last 2 years. Results: Between 2018 and 2023, the ice volume in certain sections of the cave decreased by up to 667 m³, with notable reductions in ice thickness ranging from 0.3 to 0.9 m in areas like the Small Hall and Collapsed Dome. The study also detected dynamic changes, such as the widening of the ice tunnel by 20 cm in some sections, and a vertical ice wall in Ruffinyi's Corridor showed localized volume losses up to 9 m3 (between 2018 and 2023). Additional geophysical methods - microgravimetry and ground penetrating radar - revealed an average ice thickness ranging from 10 to 25 m. Discussion: The paper not only highlights the current technological possibilities but also points out the limitations of these technologies and then sets out solutions with a proposal of technological procedures for obtaining accurate geodetic and geophysical data. [ABSTRACT FROM AUTHOR]

To improve the assembly accuracy of intelligent assembly process, a 3D surface reconstruction system based on laser scanning for assembly targets and assembly positions was designed. The light source uses a line laser to provide structured light, enabling rapid scanning of the assembly target surface on a single line. Calculate the spatial relationship between the assembly target and the assembly position through the calibration board. A position inversion optimization algorithm has been proposed. By completing the transformation of feature points from local coordinates to global coordinates. The energy distribution in the central area of the CCD image plane was simulated and analyzed under different baseline lengths, and the optimal baseline position of 35 mm was determined. The experiment is divided into two parts, and the reconstruction of the three-dimensional surface of the free-form surface target based on laser scanning was completed in the laboratory. The optimized target 3D point cloud data is evenly distributed, with almost no scattered points on the main two local surfaces, which well reflects the surface structure of the target. In the actual outdoor assembly experiment, the system cooperated with the assembly vehicle to achieve the correct assembly of the assembly. The reconstruction accuracy deviation of most test points is very small, with over 85% of test points having an error of less than 0.54 mm. The average deviation of these points before and after optimization is 0.67 mm. This system has higher application value in the field of large-scale intelligent assembly. [ABSTRACT FROM AUTHOR]

Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline's potential and effectiveness. The pipeline is based on the Open3D framework and is available open source. [ABSTRACT FROM AUTHOR]

Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations. Together with the quality of the measurement data (diverse point-cloud density, missing samples, holes induced by obstructions in the field of view, measurement noise), this creates problems that require multi-stage processing of the obtained data. The authors propose a robust methodology to process a single segmented section of the mining system. The presented approach focuses on obtaining a point cloud ready for application in the computational fluid dynamics (CFD) analysis of airflow with minimal need for additional manual corrections on the generated mesh model. This requires the point cloud to have evenly distributed points and reduced noise (together with removal of objects inside) while keeping the unique geometrical properties and shape of the scanned tunnels. Proposed methodology uses trajectory of the excavation either obtained during the measurements or by skeletonization process explained in the article. Cross-sections obtained on planes perpendicular to the trajectory are processed towards the equalization of point distribution, removing measurement noise, holes in the point cloud and objects inside the excavation. The effects of the proposed algorithm are validated by comparing the processed cloud with the original cloud and testing within the CFD environment. The algorithm proved high effectiveness in improving skewness rate of the obtained mesh and geometry mapping accuracy (standard deviation below 5 centimeters in cloud-to-mesh comparison). • Real scan data can be used for ventilation simulations of mining excavations. • Volumetric mesh from raw point cloud is of poor quality. • Improved process involves an automatic approach to generate regularized point clouds. • Uniform point distribution cuts mesh skewness from unsuitable to good quality. • The algorithm addresses noise and unnecessary objects in the tunnel section. [ABSTRACT FROM AUTHOR]

Tolerance management in the Architecture, Engineering, and Construction (AEC) sector faces challenges due to a lack of systematic scientific methods and tools. Quality issues in construction, coupled with inefficiencies, rework, and waste arising from deviation problems, hinder the sustainable development of the AEC sector. Current deviation control methods in the AEC field rely on compliance inspections and on-site rework, which are reactive and costly. In contrast, the manufacturing industry, closely linked to AEC, has developed highly automated methods for tolerance management. Although prefabricated buildings are manufactured off-site with high precision, the on-site assembly precision remains low, failing to fully leverage manufacturing industry capabilities. This research proposes a framework for predicting assembly accuracy of prefabricated buildings during the design and early construction stages. The framework, based on tolerance analysis methods from the manufacturing industry, integrates Building Information Modeling (BIM) and Terrestrial 3D Laser Scanning (TLS) technologies. This innovative approach offers a computer-aided method for conducting tolerance analysis in prefabricated buildings. A completed prefabricated building project serves as a case study, utilizing the framework to predict variations in critical dimensions, and the predictions align with measured results, demonstrating the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

The paper presents a new dry-dock method for assessing the deformation of submarine hulls using TLS point cloud data and the point cloud spatial expansion method (PCSE). The advantage of the proposed approach is the high-resolution deformation analysis that can be conducted in the case of both the availability and a lack of technical documentation on the submarine hull. The geometry assessment involves two-plane hull symmetry in longitudinal sections of a tested Kobben-class submarine located in Gdynia, Poland. The features of PCSE introduce additional geometrical parameters that are not available in the original point cloud method. The procedure for local fitting of a plane into the expansion eliminates the problem of varying densities of the hull point cloud. Accuracies of several millimetres are achieved and are applicable to multi-temporal monitoring of the deformations of submarine hulls. The assessment of similar deformations is not possible in the original point cloud method, due to the unknown parameters of the orientation and curvature of the convex cylindrical hull surface. The PCSE-based parameterisation presented here enables the creation of alternative quasi-planar point cloud projections to extract new spatial information on the object. The results of this study were verified using theoretical values derived from design data on the Kobben-class submarine, and demonstrated the effectiveness of our method in terms of detecting deformations even without design references. The proposed methodology is uniform, and can be adapted to other symmetrical structures. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) is the hottest topic of the last decade in the construction sector. BIM is interacting with other technologies toward the realization of digital twins. The integration of laser scanning technology and BIM is progressing. Increasingly, solid, mesh models are being semantically enriched for BIM. A point cloud can provide an excellent source of data for developing a BIM model. The BIM model will be refined not only geometrically but can also be saturated with non-graphical data. The problem is the lack of a clear methodology for compiling such models based on TLS and images. The research and development work between universities and companies has put modern digital solutions into practice. Thus, the purpose of this work was to develop a universal methodology for the acquisition and extraction of data from disconnected sources. In this paper, three BIM models were made based on point clouds derived from laser scanning. The case studies presented confirm the validity of the "scan to BIM approach, especially in the context of historic buildings (HBIMs). The paper posits that the integration of laser scanning, digital photogrammetry and BIM provides value in the preservation of heritage buildings. In the process of the practical work and an in-depth literature study, the ever-present limitations of BIM were identified as research challenges. The paper contributes to the discussion on the use of BIM in the design, construction and operation of buildings, including historic buildings. The acronym HBIM (heritage building information modeling) will increasingly resonate in the academic and practical work of the discipline of conservation and maintenance of historic buildings and cultural heritage sites. [ABSTRACT FROM AUTHOR]

Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a number of handcrafted features representing the local geometry around it are calculated at different scales, that is, varying the spatial extension of the local analysis. Calculating the scales at small intervals allows each feature to be accurately approximated using a smooth function and, for the problem of semantic segmentation, to be tackled using functional data analysis. We also present a step-wise method to select the optimal features to include in the model based on the calculation of the distance correlation between each feature and the response variable. The algorithm showed promising results when applied to simulated data. When applied to the semantic segmentation of a point cloud of a forested plot, the results proved better than when using a standard multiscale semantic segmentation method. The comparison with two popular deep learning models showed that our proposal requires smaller training samples sizes and that it can compete with these methods in terms of prediction. [ABSTRACT FROM AUTHOR]

In recent years, 3D digitalisation has experienced significant growth, revolutionising the way we capture, process and use geometric data. Initially conceived for industrial applications, these technologies have expanded to multiple fields, offering unprecedented accuracy and versatility. Depending on the accuracy and efficiency to be achieved in a specific field of application, and on the analytical capacity, a specific 3D digitalisation technique or another will be used. This review aims to delve into the application of 3D scanning techniques, according to the implementation sector. The optimal geometry capturing and processing 3D data techniques for a specific case are studied as well as their limitations. [ABSTRACT FROM AUTHOR]

Laser scanning data are increasingly used to segment forests into homogeneous units, called segments or stand compartments. Region growing and region merging have been traditionally employed for this purpose. Recently, alternative methods such as cellular automata (CA), self-organizing maps (SOM), and combinatorial optimization have emerged, promising more homogeneous stand delineations. However, these newer methods often require fine-tuning due to rugged stand boundaries. To address this, we propose and assess a novel hybrid approach that combines two segmentation algorithms, resulting in smoother boundaries and homogeneous stands. Our hybrid method outperforms traditional fine-tuning techniques, particularly using CA or SOM for initial segmentation and then refining it with another cellular automaton (CA'), adjusted to produce smooth boundaries. The new two-step hybrid method resulted in better segmentation results than the one-step algorithm combined with previously suggested fine-tuning methods. SOM-CA' showed the highest degree of explained variance (R2) of the LiDAR metrics used in the segmentation. CA-CA' reached almost the same R2 values with a larger average segment area. When the R2 of the LiDAR metrics was used as the criterion, the segments produced by the hybrid method outperformed the stand delineations of the current Spanish forest map with a clear margin. [ABSTRACT FROM AUTHOR]

The mobile 3D mapping systems (MMS) and the integration with Geographical Information Systems (GIS) represent an innovative and efficient approach to modern topography. It is recommended and useful for infrastructure analysis and urban planning. This modern technology provides accurate, fast, and detailed data, facilitating well-informed decision-making and contributing to the development of sustainable infrastructure in the 21st century. The purpose of this research was to use a mobile mapping system for land surveying measurement of an urban area, independent of pre-established ground control points. On the other hand, the research is supposed to demonstrate the usefulness of kinematic trajectory, digital imaging, and laser scanning in acquiring, storing, and processing point cloud data. This paper shows an affordable method and a modern scanning instrument used for mobile mapping scanning, to create a georeferenced point cloud (PC). The obtained result has centimetric precision of georeferenced point cloud data, even if the average speed of the car was about 30km/h. The trajectory adjustment with static GNSS measurements increases the accuracy of ground control points (GCPs) up to ±5cm for horizontal positioning and vertical positioning. Although this study was carried out while the system was mounted on a car the result followed the national accuracy requirements for land surveying and cadastre fields. In other words, the mobile mapping system is a faster and cost-effective in time alternative to static laser scanning and it is important to continue to explore and adopt these technological solutions to face the future challenges in the field of urban development and infrastructure. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system’s scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety.

Abstract In recent years, the application of real scene 3D technology has become widespread in urban planning and cultural heritage protection. However, there has been relatively little attention paid to the construction of real scene 3D models for special natural landscapes such as caves. Given the global distribution of karst topography and the large number of naturally developed caves with diverse types, unique landscape styles, and significant scientific value, this paper enriches the research in this field. By combining ground-based and aerial remote sensing techniques, and based on 3D laser scanning and photogrammetry, we have successfully constructed a real scene 3D model of the internal structure of a karst cave with a precision better than 4 cm. Utilizing Unmanned Aerial Vehicle (UAV) oblique photography, we established a real scene 3D model of the external karst landform with a precision better than 2 cm. We also integrated the internal and external 3D models of the cave, developing a new, complete, and high-precision method for constructing real scene 3D models of karst cave landscapes. Furthermore, we proposed a method for texture reproduction in the dark environment inside the caves, enhancing the reproduction and visual appeal of the real interior. The establishment of high-precision real scene 3D models can not only serve as an effective tool for scientific research on caves but also, as replicas of the real world, play a crucial role in public dissemination and education, thereby enhancing public understanding of cave geological landscapes.

Grain surface movement monitoring is an important part of the daily inventory inspection of national grain reserves, and is a new requirement for grain storage supervision. In order to solve the technical problems of the traditional grain surface movement monitoring, this paper proposed a laser radar-based grain surface movement monitoring method. A full-size experimental platform that can simulate the real grain pile state was established, and a high-precision laser three-dimensional measurement device was utilized to design an algorithm to identify the abnormal movement of the grain surface of the bulk grain pile based on the laser scanning point cloud data and the information of entering and exiting the warehouse operation. The algorithm was examined and verified through the experimental platform. The results showed that: the method can directly obtain high-precision coordinate information of the grain pile surface, which overcome the problem of insufficient data accuracy of image recognition technology. The constructed algorithm of grain surface motion discrimination based on the coordinate information of the grain pile shape and the data of entry and exit operation status was capable of realizing reliable quantitative computatio. The proposed method was applied to the actual grain warehouse scenario, which verified the feasibility and validity of the method, and it was able to satisfy the dynamic grain inventory supervision needs. The proposed method was applied to the actual grain silo scenario to verify the feasibility and effectiveness of the method, which can meet the demand of grain stock dynamics supervision and provide a new technology for online monitoring and early warning of grain surface variation.

The process of engineering construction typically impacts the nearby existing buildings, causing either localized or widespread deformation of these structures. The application of deformation monitoring technology enables the timely identification of damage or uneven deformation, thereby facilitating the implementation of appropriate maintenance and repair actions. This paper explores the application of ground-based synthetic aperture radar (GB-SAR) in building facade deformation monitoring. GB-SAR acquires two-dimensional (2D) images and measures deformation along the line of sight (LOS) direction through interference calculation. In order to accurately determine the location of deformation and retrieve the elevation information of image pixels, it is usually required to utilize external three-dimensional (3D) data to reconstruct the 3D coordinate of these pixels, which is known as geocoding, or georeferencing. In the case of the GB-SAR system, it is necessary for the observer to measure the angles between the antenna orientation, the radar movement direction and the reference plane of the local surveying coordinate system. Nevertheless, the measurement of these attitude angles may be overlooked in practice, and the impact of small attitude angles or their measurement errors on GB-SAR geocoding may be disregarded, which directly affects the slant range projection (SRP) calculation of the external 3D data, thereby leading to biased 3D coordinate reconstruction of GB-SAR pixels. This paper proposes a method to calculate the SRP coordinates of point cloud data considering the attitude inclination angles of radar sensor, and perform matching calculations on the high-quality pixels (HQPs) with the point cloud after SRP to extract building facade deformation. The results demonstrate that the method is capable of effectively mitigating the influence of the radar attitude inclination angles, resulting in a reduction of the average deviation of 3D coordinates to approximately one-third of the value without consideration of the sensor's attitude in a real-world building monitoring experiment. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system's scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety. [ABSTRACT FROM AUTHOR]

Point cloud data acquired by scanning with Light Detection and Ranging (LiDAR) devices typically contain irregular objects, such as trees, which lead to low classification accuracy in existing point cloud classification methods. Consequently, this paper proposes a deep point feature aggregation network (DPFANet) that integrates adaptive graph convolution and space-filling curve sampling modules to effectively address the feature extraction problem for irregular object point clouds. To refine the feature representation, we utilize the affinity matrix to quantify inter-channel relationships and adjust the input feature matrix accordingly, thereby improving the classification accuracy of the object point cloud. To validate the effectiveness of the proposed approach, a TreeNet dataset was created, comprising four categories of tree point clouds derived from publicly available UAV point cloud data. The experimental findings illustrate that the model attains a mean accuracy of 91.4% on the ModelNet40 dataset, comparable to prevailing state-of-the-art techniques. When applied to the more challenging TreeNet dataset, the model achieves a mean accuracy of 88.0%, surpassing existing state-of-the-art methods in all classification metrics. These results underscore the high potential of the model for point cloud classification of irregular objects. [ABSTRACT FROM AUTHOR]

Installation quality check is essential for ensuring the construction quality of prefabrication construction. The existing techniques for assessing the installation quality of prefabricated wall panels heavily depend on manual inspection and contact-type measurements, which is labor intensive and slow. Laser scanning was previously adopted in construction quality check, however, few studies have focused on using laser scanners to assess the verticality of prefabricated wall panels, and no method has been developed for effective practical implementation. This study proposes a streamlined laser scanning approach for onsite verticality check of prefabricated wall panels. Based on systematic experiments of using the point cloud data collected by different types of laser scanners, and 25 prefabrication wall panels of four shapes, this study validates the proposed method and compares the use of different laser scanners. To facilitate an effective streamlined process for practical use, this study identifies the point cloud segmentation parameters under different laser scanning data sets and suggests suitable parameters for these case scenarios. These parameters can be adopted directly or used as references for practical application of the proposed laser scanning method in the installation verticality check. This study contributes to improving the efficiency of installation quality check of prefabrication construction, and facilitating the digital evolution of the construction industry. Practical Applications: Checking the verticality of the installed prefabricated wall panels is crucial in construction quality control. However, traditional methods for assessing the installation quality of prefabricated wall panels heavily depend on manual inspection and contact-type measurements, which is labor-intensive, slow, and costly. For project involves a large number of same or similar type of prefabricated construction elements, this repetitive work also causes human fatigue and in-efficiency. This paper proposes a laser scanning method to streamline the quality check process for the installation of prefabricated wall panels. By systematically experimenting with the point cloud data collected by different types of laser scanners for various wall panels of different shapes, this study validates the effectiveness of the proposed method. Another major contribution of this research is preidentification of optimal segmentation parameters for laser scanning point cloud. This means construction professionals can use these parameters directly or as references for identifying suitable segmentation parameters for other projects. The streamlined laser scanning method contributes greatly to improving the efficiency of installation quality check of prefabrication construction practice, especially when large number of identical or similar elements are used. [ABSTRACT FROM AUTHOR]

Pavement skid resistance is significant for driving safety. British Pendulum Number (BPN) is commonly used as a low-speed skid resistance indicator, whereas sometimes it is impractical for data collection on roads in service. Since skid resistance is greatly affected by pavement surface texture, this research aims to evaluate pavement surface texture comprehensively and estimate the low-speed friction BPN from road surface texture on macro- and micro- scale. Asphalt Concrete (AC) and Stone Mastic Asphalt (SMA) were included. Road surface texture was evaluated from four aspects, texture depth, amplitude-related Root Means Square (RMS), elevation variances corresponding to different wavebands and texture spectral analysis. Texture depth indicators include Mean Texture Depth (MTD) and Mean Profile Depth (MPD). Elevation variances with three wavebands, from 5 mm to 50 mm, from 0.5 mm to 5 mm and from 0.024 mm to 0.5 mm respectively, were obtained. The results show that MPD is well correlated with MTD. Elevation variances with different wavebands demonstrates that the elevation variance of macro-texture with long wavelengths from 5 mm to 50 mm dominates the total variance. Spectral analysis shows that texture level is larger when the wavelength is beyond 4 mm, which is consistent with elevation variances. A linear regression between BPN and single texture index, as well as multiple linear regression analysis were conducted. The former regression result indicates that it is not feasible to estimate BPN using single index due to low correlation coefficient R2. The latter shows that the BPN can be estimated from texture levels corresponding to 64 mm and 2 mm, and the micro-texture. The R2 can be up to 0.684. This research will contribute to fast acquisition of BPN from pavement surface texture, thus improving skid resistance. [ABSTRACT FROM AUTHOR]

Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide. [ABSTRACT FROM AUTHOR]

Freeform surfaces like tensor product B-spline surfaces have been proven to be a suitable tool to model laser scanner point clouds, especially those representing artificial objects. However, when it comes to the modelling of point clouds representing natural surfaces with a lot of local structures, tensor product B-spline surfaces reach their limits. Refinement strategies are usually used as an alternative, but their functional description is no longer nearly as compact as that of classical tensor product B-spline surfaces, making subsequent analysis steps considerably more cumbersome. In this publication, the approximation quality of classical tensor product B-spline surfaces is improved by means of local parameterization. By using base surfaces with a local character, relevant information about local structures of the surface to be estimated are stored in the surface parameters during the parameterization step. As a consequence, the resulting tensor product B-spline surface is able to represent these structures even with only a small number of control points. The developed locally parameterized B-spline surfaces are used to model four data sets with different characteristics. The results reveal a clear improvement compared to the classical tensor product B-spline surfaces in terms of correctness, goodness-of-fit and stability. [ABSTRACT FROM AUTHOR]

Trace detection in tunnel faces is critical for preventing failures such as spalling in tunnel excavations. This paper proposes a novel method, referred to as the Roughness-CANUPO (R–C) Method, for the automatic detection of these traces in 3D point clouds obtained from tunnel faces, in which its combines roughness analysis with the CANUPO machine learning algorithm to improve the accuracy and efficiency of trace detection. In this study, images were collected from six sites and point cloud data of 6 sites were obtained by 3D reconstruction. Points representing rock masses in the collected 3D point cloud data can be filtered by roughness analysis in the R–C methods. Roughness analysis can not only significantly reduce the analysis time of CANUPO machine learning, but also increase the accuracy of CANUPO analysis. In this paper, the local neighbourhood radius (LNR) and filtering ratio optimized in the roughness analysis and the roughness for filtering discontinuities were evaluated. The filtered point cloud data automatically extract only the trace point cloud by the CANUPO classifier. The extracted trace was projected onto the original data to accurately designate the location of the trace. Conducting the R–C method yields a significant result regarding discontinuity traces classification by reaching the level of accuracy ranging from 95.9% to 98.9%. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology with which to measure the slight erosion undergone by valuable heritage pieces in a short period of time. The problems dealt with by our method concern the irregular (free-shape) geometry of the piece to be measured, the difficulty involved in achieving the fine registration required among different samples of the piece, the slight loss of material in a short period of time and the suitable definition of descriptors that will help to carry out a reliable qualitative and quantitative analysis. This document proposes a basic methodology that is able to tackle each of the aforementioned problems. An in-depth explanation of the degradation analysis has been carried out on a representative case study, including the qualitative and quantitative procedures employed. The results obtained lead us to believe that this methodology could be reproduced and applied to similar degradation problems related to old buildings. [ABSTRACT FROM AUTHOR]

In historic wooden structures, deformations can be influenced by factors that are negligible within the first 50 years of the building's existence. In attics where the roof covering is made of metal sheets, the air and structural elements heat up during the day and cool down rapidly at night. This phenomenon, over a long period, can cause micro-cracks and surface material degradation. For the study, historic buildings meeting the criteria for service class II according to PN-EN 1995-1-1 were selected. Measurements of deflections, humidity, and modulus of elasticity of the wood were conducted. The actual deflections of the examined structures were found to be 28% to 37% higher than those obtained from numerical calculations, indicating ongoing rheological processes in the wooden structures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper we propose a monitoring method that allows the inclusion of point clouds into a geodetic monitoring network. Consequently, network adjustment and a rigorous deformation analysis can be performed allowing consistent error propagation and trustful significance calculation. We introduce a supervised pipeline based on ICP-matching of small-scale laser scan patches. It is specially designed for geo-monitoring applications and allows to improve the spatial resolution as well as the network geometry of monitoring networks in inaccessible hazardous areas in the mountains, where the installation of a sufficient number of targets is difficult. We apply our method to two datasets. The first is a monitoring setup in the laboratory, where we establish the parameters for the supervised patch selection and demonstrate how the network geometry is improved. Second is the real case study of Mt. Hochvogel, where the proposed method helps to clearly improve the spatial resolution of deformation vectors. [ABSTRACT FROM AUTHOR]

For more than a dozen or so years now, there has been growing interest in the use of modern laser scanning measurement methods in numerous mining operations engaged in underground excavation. However, the simple possession of a scanner does not guarantee satisfactory measurement results. This study sets out the results of scanning mine excavations in an active mine and describes the current guidelines on various aspects of the measurement process. These guidelines were developed on the basis of several hundred measurements carried out over the last dozen or so years. This study also outlines the typical measurements errors observed over the course of many years. These errors, resulting partly from hardware limitations and partly from human error when planning or actually performing measurements, were an important factor behind the introduction of standards regulating underground measurements. This study discusses in detail not only scanning that utilises traditional stationary laser scanners but also scanning based on mobile scanners. It also presents possible areas of future technological development in line with global trends. [ABSTRACT FROM AUTHOR]

In recent years, the application of real scene 3D technology has become widespread in urban planning and cultural heritage protection. However, there has been relatively little attention paid to the construction of real scene 3D models for special natural landscapes such as caves. Given the global distribution of karst topography and the large number of naturally developed caves with diverse types, unique landscape styles, and significant scientific value, this paper enriches the research in this field. By combining ground-based and aerial remote sensing techniques, and based on 3D laser scanning and photogrammetry, we have successfully constructed a real scene 3D model of the internal structure of a karst cave with a precision better than 4 cm. Utilizing Unmanned Aerial Vehicle (UAV) oblique photography, we established a real scene 3D model of the external karst landform with a precision better than 2 cm. We also integrated the internal and external 3D models of the cave, developing a new, complete, and high-precision method for constructing real scene 3D models of karst cave landscapes. Furthermore, we proposed a method for texture reproduction in the dark environment inside the caves, enhancing the reproduction and visual appeal of the real interior. The establishment of high-precision real scene 3D models can not only serve as an effective tool for scientific research on caves but also, as replicas of the real world, play a crucial role in public dissemination and education, thereby enhancing public understanding of cave geological landscapes. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In current engineering and research, the indices of cross-sectional deformation extracted from point clouds are primarily convergent deformation, which can hardly reflect the deformation characteristics of sectionally continuous deformation properties of shield tunnel linings under the action of a joint. To fully extract the cross-sectional deformation characteristics from point clouds, a deformation characterization method, which takes joints as boundary to fit each segment separately in a polar coordinate system using Fourier functions, is proposed. The fitted curves of each segment are then meshed based on the same density, and the node coordinates are tracked to derive the displacement field of each segment. Full-scale experiments and numerical models are used to validate the reliability of this method. In addition, theoretical analysis and numerical experiments are conducted to examine the influence of key factors (such as the types of fitting functions, influence of measurement errors, and selection of reference system) on deformation extraction results. The findings reveal the following: (1) The higher the order of the Fourier function used for fitting, the more detailed the fitting of the segment lining profile. (2) Under the effect of measurement errors, the application of high-order Fourier functions is susceptible to overfitting, which results in an increase in the deformation extraction error. (3) The proposed method shows its effect and feasibility in actual shield tunnel engineering. [ABSTRACT FROM AUTHOR]

This study investigates the utilization of 3D laser scanning, GPS, BIM and GIS technologies for safeguarding historical wooden structures. As a case study, it focuses on two historic Chinese wooden buildings, the Guanyin Pavilion and Tangwang Palace, renowned for their exceptional historical significance and intricate timber architecture. The Guanyin Pavilion was subjected to high-precision modeling and tilt monitoring, which found that certain columns had a maximum tilt angle of 0.7892°. Additionally, a general tilt tendency was seen across various structural elements. The measurements from the point cloud data exhibited an error range of ±10 mm. However, the relative errors in smaller components approached 2.5%, which necessitated regular verification through human measurements. Using GIS and BIM technology in Tangwang Palace facilitated the meticulous documentation and dynamic supervision of architectural components, encompassing historical development and present state. The maintenance records revealed persistent problems with the roof tiles and wooden beams, exposing structural vulnerabilities. During various maintenance intervals, it was observed that the roof tiles were frequently loose and separated, indicating potential flaws in the design or materials used. The application of these modern technologies enhances the effectiveness and precision of conservation efforts and provides a robust scientific foundation for the continuous monitoring and study of cultural heritage. [ABSTRACT FROM AUTHOR]

Copyright of Architectonics & Architecture / Építés - Építészettudomány is the property of Akademiai Kiado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IntroductionDobšiná Ice Cave (Slovakia) has attracted the attention of many researchers since its discovery more than 150 years ago. Although the cave is located outside the high-mountain area, it hosts one of the largest volumes of underground perennial ice. The topographic mapping of this unique UNESCO Natural Heritage site has led to several historical surveys. In the last decades of rapid climate change, this natural formation has been subject to rapid changes that are dynamically affecting the shape of the ice body. Increased precipitation, the rise in year-round surface temperatures, and the gravity cause significant shape changes in the ice filling.MethodsThis paper describes modern technological tools to comprehensively survey and evaluate interannual changes in both the floor and wall of the underground ice body. Technologies such as digital photogrammetry, in combination with precise digital tacheometry and terrestrial laser scanning, make it possible to detect ice accumulation and loss, including the effect of sublimation due to airflow, as well as sliding movements of the ice body to the lower part of the cave. To get a comprehensive model of the ice volume, geophysical methods (microgravimetry and ground penetrating radar) have been added to determine the thickness of the floor ice in the upper parts of the cave in the last 2 years.ResultsBetween 2018 and 2023, the ice volume in certain sections of the cave decreased by up to 667 m³, with notable reductions in ice thickness ranging from 0.3 to 0.9 m in areas like the Small Hall and Collapsed Dome. The study also detected dynamic changes, such as the widening of the ice tunnel by 20 cm in some sections, and a vertical ice wall in Ruffinyi’s Corridor showed localized volume losses up to 9 m3 (between 2018 and 2023). Additional geophysical methods - microgravimetry and ground penetrating radar - revealed an average ice thickness ranging from 10 to 25 m.DiscussionThe paper not only highlights the current technological possibilities but also points out the limitations of these technologies and then sets out solutions with a proposal of technological procedures for obtaining accurate geodetic and geophysical data.

Airborne and terrestrial laser scanners have traditionally been used as specialised toolsets for three-dimensional scene capture in engineering, providing highly accurate measurements with increasingly minimal human interaction. However, commercial or engineering-grade scanning instruments remain expensive and sensitive, requiring costly routine calibrations to ensure their optimum functionality. The recent inclusion of laser scanning sensors by mobile phone corporations such as Apple Computer Inc. is now analogous to the integration of Global Navigation Satellite Systems (GNSS) and cameras into smartphones as seen decades ago. Likely, these initial efforts to include the scanning sensor in mobile phones will see rapid improvements in the application and accuracy of the sensor to serve the growing need for scanning applications for transdisciplinary users. However, there is a limited amount of literature that benchmarks the emerging and low-cost scanning sensors to existing commercial ones to inform practice, thus prompting a need for researchers to evaluate and provide scientific evidence that can inform multidisciplinary scanning. It was noted that there was some absolute positional shift and scan drift in the iPhone Light Detection and Ranging (LiDAR) data. The researchers therefore investigated the extent to which the accuracy of laser scanning tools available within the iPhone 12 Pro compared to engineering-grade laser scanners. Outcomes from the study showed that iPhone scanners can deliver the required models, despite being unstable in dynamic environments when pitched against engineering-grade LiDAR scanners. The research recommends that stabilisers, such as stabilising gimbals or enhanced GNSS receivers, be used in practice to achieve improved accuracy from the mobile phone LiDAR. Significance: Laser scanners offer multiple advantages for modelling features in three dimensions in diverse applications, including documentation, archaeology, environmental modelling and mapping. However, the cost of entry to acquire scan data has been a limitation to its wide-scale use across multiple disciplines. This study demonstrated from an accuracy-based perspective that iPhone scanners can deliver the required models addressing different model purposes, despite being slightly unstable when pitched against engineering-grade Light Detection and Ranging (LiDAR) scanners. The results are significant in reinforcing the competence of low-cost tools in increasing access and use of this technology in curating three-dimensional models for multidisciplinary work.

To improve the assembly accuracy of intelligent assembly process, a 3D surface reconstruction system based on laser scanning for assembly targets and assembly positions was designed. The light source uses a line laser to provide structured light, enabling rapid scanning of the assembly target surface on a single line. Calculate the spatial relationship between the assembly target and the assembly position through the calibration board. A position inversion optimization algorithm has been proposed. By completing the transformation of feature points from local coordinates to global coordinates. The energy distribution in the central area of the CCD image plane was simulated and analyzed under different baseline lengths, and the optimal baseline position of 35 mm was determined. The experiment is divided into two parts, and the reconstruction of the three-dimensional surface of the free-form surface target based on laser scanning was completed in the laboratory. The optimized target 3D point cloud data is evenly distributed, with almost no scattered points on the main two local surfaces, which well reflects the surface structure of the target. In the actual outdoor assembly experiment, the system cooperated with the assembly vehicle to achieve the correct assembly of the assembly. The reconstruction accuracy deviation of most test points is very small, with over 85% of test points having an error of less than 0.54 mm. The average deviation of these points before and after optimization is 0.67 mm. This system has higher application value in the field of large-scale intelligent assembly.

Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications and environments. One of the main downsides of deep learning-based approaches is the need for extensive training datasets, i.e. LiDAR point clouds that have been annotated for target tasks by human experts. One strategy for addressing this issue is the use of simulated/synthetic data (with automatically generated annotations) for training models which can then be deployed on real target data/environments. This paper explores using synthetic data of realistic forest trees to train deep learning models for tree part segmentation from real forest LiDAR data. We develop a new pipeline for generating high-fidelity simulated LiDAR scans of synthetic forest trees and combine this with an unsupervised domain adaptation strategy to adapt models trained on synthetic data to LiDAR data captured in real forest environments.Models were trained for semantic segmentation of tree parts using a PointNet++ architecture and evaluated across a range of medium to high-resolution laser scanning datasets collected across both ground-based and aerial platforms in multiple forest environments. Results of our work indicated that models trained on our synthetic data pipeline were competitive with models trained on real data, in particular when real data came from non-target sites, and our unsupervised domain adaptation method further improved performance. Our approach has implications for reducing the burden required in manual human expert annotation of large LiDAR datasets required to achieve high-performance from deep learning methods for forest analysis. The use of synthetically-trained models shown here provides a potential way to reduce the barriers to the use of deep learning in large-scale forest analysis, with implications to applications ranging from forest inventories to scaling-up in-situ forest phenotyping.

The scanning of weld seams can be used to evaluate the local weld toe geometry for fatigue assessments. Laser scanned weld seam profiles often contain noise which complicates the accurate measurement of the weld toe geometry. For that reason, filtering of the scanned data is necessary. The issue at hand is that a filtering method can significantly affect the measurement results. Therefore, a calibration of the filter input parameters is needed. In this study, a calibration method for filtered laser-scanned weld profiles is presented by using artificial weld toe geometries. The adjustment of different filter functions is achieved by using an optimization method on predefined weld toes with an artificial noise. The resulting input data for the filter functions is tested on a real specimen to verify the method. Through the calibration method it is possible to achieve satisfactory measurement results with precisely set input parameters for the filter functions. The most suitable filter functions for the measurement of the weld toe are the Gaussian and the Lowpass filter. Both functions are adequate as a universally applicable filter. For the evaluation of the measurement results of the radii and angles, a tolerance range is introduced, which is defined by the theoretically minimum measurable radii and angles. Using an adjusted Lowpass filter and a point distance of 0.07 mm set by the laser scanner, a measurement within the tolerance range of 0.2 mm is achievable for the weld toe radius. For the weld toe angle, the tolerance range of 1.5° is achieved for the majority of measurements.

Laser scanning data are increasingly used to segment forests into homogeneous units, called segments or stand compartments. Region growing and region merging have been traditionally employed for this purpose. Recently, alternative methods such as cellular automata (CA), self-organizing maps (SOM), and combinatorial optimization have emerged, promising more homogeneous stand delineations. However, these newer methods often require fine-tuning due to rugged stand boundaries. To address this, we propose and assess a novel hybrid approach that combines two segmentation algorithms, resulting in smoother boundaries and homogeneous stands. Our hybrid method outperforms traditional fine-tuning techniques, particularly using CA or SOM for initial segmentation and then refining it with another cellular automaton (CA’), adjusted to produce smooth boundaries. The new two-step hybrid method resulted in better segmentation results than the one-step algorithm combined with previously suggested fine-tuning methods. SOM-CA’ showed the highest degree of explained variance (R2) of the LiDAR metrics used in the segmentation. CA-CA’ reached almost the same R2 values with a larger average segment area. When the R2 of the LiDAR metrics was used as the criterion, the segments produced by the hybrid method outperformed the stand delineations of the current Spanish forest map with a clear margin.