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The present study investigates the impact of different heat transfer approaches on the precision of geometry and solidification behavior in additively manufactured thin clad walls. Employing a 2D laser scanner to assess dimensional accuracy, the study demonstrates that thin clad walls produced on a copper base plate with forced air convection exhibit notably superior accuracy compared to those on a mild steel substrate with natural convection. Microstructure analysis unveils distinct characteristics: Thin clad walls printed with natural convection on mild steel display a coarse columnar dendritic structure marked by increasing primary dendrite arm spacing (PDAS) from bottom to top (10.4 ± 0.7 to 14.5 ± 3 µm). In contrast, forced convection leads to a fine cellular and columnar dendritic microstructure with minor PDAS variation (4.6 ± 0.9 to 7.4 ± 1.7 µm) along the entire vertical span. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature 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.)

Matching the terrestrial laser scan point clouds with the unmanned aerial oblique photography point clouds of high-rise irregular buildings can make them complementary. The challenge lies in the different sources and qualities, besides arbitrary angular deviations of the two types of point clouds. We propose a registration method based on point and line features, which automatically recognises the features of these point clouds using normal vectors and 3D-Harris, and solves the registration parameters through rigorous adjustment. The registration model, process and method are introduced. Finally, through the analysis of real case examples, it was found that the registration result of this method was better than the traditional Iterative Closest Point (ICP) in the analysed cases, the proposed registration method not only exhibits good automation but also has the ability to ignore various differences in heterogeneous point clouds, thereby improving the accuracy and efficiency of registration for such point clouds. [ABSTRACT FROM AUTHOR]

Effective forest management and conservation are increasingly critical in addressing the challenges posed by climate change. Advances in remote sensing technologies, such as terrestrial laser scanning, offer promising tools for more accurate assessments in forestry research. This study explores the application of TLS in biomass estimation by conducting a bibliometric analysis of scientific articles indexed in Scopus and the Web of Science. By examining the literature from 2010 to 2024, the study identifies key trends, knowledge gaps, and emerging research opportunities, as well as practical applications in forest management and conservation. The analysis reveals a significant rise in scientific output on TLS, with an average annual growth rate of 8.16%. The most cited works address biomass estimation at the individual tree level using laser scanning data. China and the United States lead in the publication volume with 11 articles. The collaboration network highlights research disparities among regions such as Latin America. Overall, TLS has proven effective for the non-destructive measurement of forest variables and biomass. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze the application of Total Station and Laser Scanner in geodesy surveys to obtain accurate coordinates and evaluate polygon shapes. The Total Station method is used to measure the angle and distance between points, while the Laser Scanner is used to generate point cloud data which is then processed. The data from the two methods were compared to determine the accuracy of the coordinates using the Root-Mean-Square Error (RMSE). The results showed that the Laser Scanner provided higher accuracy than the Total Station, with an RMSE value for horizontal coordinates of 0.038 meters and an elevation of 0.020 meters. Field evaluation also validates the accuracy of the scan results with direct measurements using a hand meter. This study not only confirms the superiority of the Laser Scanner in producing accurate geospatial data, but also supports the development of methodologies and technologies in geodesy surveys. The results of this research make an important contribution to the development of civil engineering science, especially in the context of 3D mapping and its application in infrastructure and construction projects. [ABSTRACT FROM AUTHOR]

BIM modeling of existing buildings – a study on effort and efficiency In the project "FHprofUnt 2018: BIM Bauen im Bestand (BIM‐BiB)", funded by the Federal Ministry of Education and Research, the campus of Trier University of Applied Sciences was surveyed using terrestrial laser scanners and the data obtained was transferred to BIM models of the campus buildings. Here, the efficiency of as‐is surveys using 3D scanners, point cloud generation and post‐processing, as well as BIM modeling of existing buildings is examined on the basis of the effort values obtained. The results show that the use of terrestrial laser scanners for as‐is surveys is an efficient method, but the processing and creation of high‐quality point clouds is still very time‐consuming and the actual BIM modeling greatly exceeds the two first‐mentioned work areas in terms of effort. To explain the results presented, the current state of the art and the project‐specific problems are discussed. The data obtained and its evaluation reveal weaknesses in the efficiency of the methods available today and shed light on research opportunities to make the process of modeling existing buildings more economical. [ABSTRACT FROM AUTHOR]

In large-scale poultry farming, real-time online measurement of egg weight and shape parameters remains a challenge. To address this, we developed FEgg3D, a non-contact dynamic measuring device based on a self-designed laser scanner. The device employed a subset of the point cloud generated to predict the shape parameters and weight of eggs using machine learning algorithms. Different colors and sizes of eggs on various backgrounds were scanned using FEgg3D mounted on a gantry system. Our results demonstrated the following: (1) The Support Vector Regression (SVR) model was optimal for major axis length estimation, with an R2 of 0.932 using six laser lines and eight points per line. (2) The Gaussian Process Regression (GPR) model excelled in minor axis length estimation, achieving an R2 of 0.974 with six laser lines and 16 points per line. (3) SVR was optimal for volume estimation, attaining an R2 of 0.962 with six laser lines and 16 points per line. (4) GPR showed superior performance in weight prediction, with an R2 of 0.964 using five laser lines and 16 points per line. Including density features significantly improved accuracy to an R2 of 0.978. This approach paves the way for advanced online egg measurement in commercial settings. [ABSTRACT FROM AUTHOR]

Effective forest management and conservation are increasingly critical in addressing the challenges posed by climate change. Advances in remote sensing technologies, such as terrestrial laser scanning, offer promising tools for more accurate assessments in forestry research. This study explores the application of TLS in biomass estimation by conducting a bibliometric analysis of scientific articles indexed in Scopus and the Web of Science. By examining the literature from 2010 to 2024, the study identifies key trends, knowledge gaps, and emerging research opportunities, as well as practical applications in forest management and conservation. The analysis reveals a significant rise in scientific output on TLS, with an average annual growth rate of 8.16%. The most cited works address biomass estimation at the individual tree level using laser scanning data. China and the United States lead in the publication volume with 11 articles. The collaboration network highlights research disparities among regions such as Latin America. Overall, TLS has proven effective for the non-destructive measurement of forest variables and biomass.

Abstract Building information modeling (BIM) can greatly improve the management and planning of historic building conservation projects. However, implementing BIM in the heritage has many challenges, including issues with modeling irregular features, surveying data occlusions, and a lack of predefined libraries of parametric objects. Indeed, surface features can be manually distinguished and segmented depending on the level of human involvement during data scanning and BIM processing. This requires a significant amount of time and resources, as well as the risk of making too subjective decisions. To address these bottlenecks and improve BIM digitization of building geometry, a novel deep learning based scan-to-HBIM workflow is used during the recording of the historic building in historic Jeddah, Saudi Arabia, a UNESCO World Heritage site. The proposed workflow enables access to laser scanner and unmanned aerial vehicle imagery data to create a complete integrated survey using high-resolution imagery acquired independently at the best position and time for proper radiometric information to depict the surface features. By employing deep learning with orthophotos, the method significantly improves the interpretation of spatial weathering forms and façade degradation. Additionally, an HBIM library for Saudi Hijazi architectural elements is created, and the vector data derived from deep learning-based segmentation are accurately mapped onto the HBIM geometry with relevant statistical parameters. The findings give stakeholders an effective tool for identifying the types, nature, and spatial extent of façade degradation to investigate and monitor the structure.

Abstract Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building.

Building information modeling (BIM) can greatly improve the management and planning of historic building conservation projects. However, implementing BIM in the heritage has many challenges, including issues with modeling irregular features, surveying data occlusions, and a lack of predefined libraries of parametric objects. Indeed, surface features can be manually distinguished and segmented depending on the level of human involvement during data scanning and BIM processing. This requires a significant amount of time and resources, as well as the risk of making too subjective decisions. To address these bottlenecks and improve BIM digitization of building geometry, a novel deep learning based scan-to-HBIM workflow is used during the recording of the historic building in historic Jeddah, Saudi Arabia, a UNESCO World Heritage site. The proposed workflow enables access to laser scanner and unmanned aerial vehicle imagery data to create a complete integrated survey using high-resolution imagery acquired independently at the best position and time for proper radiometric information to depict the surface features. By employing deep learning with orthophotos, the method significantly improves the interpretation of spatial weathering forms and façade degradation. Additionally, an HBIM library for Saudi Hijazi architectural elements is created, and the vector data derived from deep learning-based segmentation are accurately mapped onto the HBIM geometry with relevant statistical parameters. The findings give stakeholders an effective tool for identifying the types, nature, and spatial extent of façade degradation to investigate and monitor the structure. [ABSTRACT FROM AUTHOR]

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings. [ABSTRACT FROM AUTHOR]

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment. [ABSTRACT FROM AUTHOR]

When evaluating the shear strength of rock mass discontinuities, certain challenges arise due to the difficulty in quantifying the roughness characteristics of surfaces and the strength of asperities. Recent research has focused on enhancing techniques for assessing these characteristics and exploring the application of laser scanning to aid in evaluating discontinuity features. The analysis of reflectivity values (I) obtained through a laser scanner survey presents an efficient method for assessing mechanical characteristics, such as joint compressive strength (JCS). Reflectivity measurements demonstrate correlations with Schmidt hammer rebound values (r). The laser scanner technique would enable the measurement of JCS without the direct application of the Schmidt hammer on rocks in areas where rebound values (r) measurements are unavailable. The use of a laser scanner allows for the acquisition of high-precision geometrical information concerning the 3D roughness and anisotropy of rock surfaces. In this study, an innovative technique was introduced that utilizes laser scanner data from six previous experimental surveys conducted on rock formations in Southern Italy. This technique facilitates the evaluation of roughness profiles, considering potential variations along kinematically admissible sliding directions, allowing for the estimation of the Joint Roughness Coefficient (JRC). This new methodology aids in evaluating the parameters of Barton's equation to determine the strength characteristics of rock mass discontinuities. [ABSTRACT FROM AUTHOR]

Copyright of Turkey Lidar Journal / Türkiye Lidar Dergisi is the property of Mersin University 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.)

(1) The early assessment of wood quality, even while trees are standing, provides significant benefits for forest management, sales efficiency, and market diversification. Its definition cannot be in absolute terms but must always be linked to the material's intended use. (2) In this contribution, a review of the scientific literature is given to discuss the visually evaluable attributes that define wood quality in standing trees, the applicability of the techniques used for their assessment, and the effectiveness of these attributes and technologies in predicting quality, to finally highlight future research needs. (3) The visual characteristics generally used to evaluate wood quality are linked to stem form and dimension, branchiness, and stem damage, but their assessment is challenging due to time and resource constraints. To address these challenges, laser-based and image-based techniques have been applied in field surveys. (4) Laser scanners offer detailed and accurate measurements. Photogrammetry, utilizing images to reconstruct 3D models, provides a cost-effective and user-friendly alternative. Studies have demonstrated the effectiveness of these tools in surveying the visible properties of stems and branches, but further development is necessary for widespread application, particularly in software development, with faster and more effective algorithmic advancements for automatic recognition and subsequent measurement of pertinent characteristics being critical for enhancing tool usability. (5) However, predicting wood quality from these surveys remains challenging, with a limited correlation between the visible tree characteristics assessed and the sawn product quality. Empirical studies evaluating products downstream in the forest-wood supply chain could provide valuable insights. In this sense, the implementation of traceability systems could facilitate the linkage between data on standing trees and the quality of the sawn product. Also, further research is needed to develop models that can accurately predict internal tree characteristics and their impact on product quality. [ABSTRACT FROM AUTHOR]

The dimensional accuracy of a laser scanner has been extensively evaluated using various measurement methods and diverse reference standards. This study specifically focuses on two key considerations. Firstly, it assesses the dimensional accuracy of the laser scanner by employing another laser scanner, a handheld scanner, as the reference measurement method. Secondly, the study involves the use of three spheres fixed on each wall in both coplanar and non-coplanar positions within a laboratory room at SENAI ISI-SIM. The primary objective is to determine the dimensional accuracy between the centers of the coplanar and non-coplanar spheres up to 10 m. The comparison includes measurement uncertainties, as per ISO GUM standards, obtained using the laser scanner in a laboratory setting with controlled temperature and humidity. Analyzing non-coplanar dimensional accuracy enhances our understanding of the metrological performance of the laser scanner, particularly when assessing the dimensions of objects positioned randomly within a scanning scene. [ABSTRACT FROM AUTHOR]

Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building. [ABSTRACT FROM AUTHOR]

Recently, robots for medical services are attracting attention as medical technology advances and interest in health increases. In particular, social robot technology plays an important role in interacting with subjects and providing health information using IoT sensors, and among these, gait information monitoring is used as an crucial tool in the medical field. There are several methods for monitoring gait information, but in this paper, a 2D laser scanner mounted on a robot was used to analyze the gait of a monitored subject, and the face-to-face posture control of the robot had a significant impact on gait pattern recognition, especially when walking at a certain distance. The results showed that it showed higher accuracy than wearable devices in the above walking situations. Accordingly, the possibility of providing medical information, such as analysis of the subject's gait, was confirmed as the service robot interacts with a walking human through face-to-face posture control. [ABSTRACT FROM AUTHOR]

The dimensional accuracy of a laser scanner has been extensively evaluated using various measurement methods and diverse reference standards. This study specifically focuses on two key considerations. Firstly, it assesses the dimensional accuracy of the laser scanner by employing another laser scanner, a handheld scanner, as the reference measurement method. Secondly, the study involves the use of three spheres fixed on each wall in both coplanar and non-coplanar positions within a laboratory room at SENAI ISI-SIM. The primary objective is to determine the dimensional accuracy between the centers of the coplanar and non-coplanar spheres up to 10 m. The comparison includes measurement uncertainties, as per ISO GUM standards, obtained using the laser scanner in a laboratory setting with controlled temperature and humidity. Analyzing non-coplanar dimensional accuracy enhances our understanding of the metrological performance of the laser scanner, particularly when assessing the dimensions of objects positioned randomly within a scanning scene.

In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor. [ABSTRACT FROM AUTHOR]

Constructing cruise-ship hull is long, complex and requires relatively precise metal carpentry, welding and assembly operations. Timely detection and correction of anomalies is vital to the sustainability of the process, in terms of reducing production time and costs. This paper presents a novel statistical quality control (SQC) methodology for monitoring the workshop for the production of so-called "modules" within shipyards, i.e., roughly parallelepiped sub-assemblies of large dimensions (on the order of several tens of meters on each side), obtained through manual carpentry and welding operations. The proposed methodology adopts a standardized p control chart with samples of variable size, incorporating two elements: (i) it accounts for the high level of customization of modules, and (ii) it takes into consideration the measurement uncertainty associated with the large-volume metrology instrument employed for conformity verification (such as a state-of-the-art Leica RTC360 laser scanner), following the ISO 14253-1:2017 standard. A real-world case study at an Italian Fincantieri S.p.A. shipyard demonstrates its practical application. [ABSTRACT FROM AUTHOR]

Copyright of Japanese Journal of JSCE / Doboku Gakkai Ronbunshu is the property of Japan Society of Civil Engineers 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.)

Knowledge of fracture properties and associated flow processes is important for geoscience applications such as nuclear waste disposal, geothermal energy and hydrocarbons. An important tool established in recent years are hydro-mechanical models which provide a useful alternative to experimental methods determining single fracture parameters such as hydraulic aperture. A crucial issue for meaningful numerical modeling is precise imaging of the fracture surfaces to capture geometrical information. Hence, we apply and compare three distinct fracture surface imaging methods: (1) handheld laser scanner (HLS), (2) mounted laser scanner (MLS) and (3) Structure from Motion (SfM) to a bedding plane fracture of sandstone. The imaging reveals that the resolution of the fracture surface obtained from handheld laser scanner (HLS) is insufficient for any numerical simulations, which was therefore rejected. The remaining surfaces are subsequently matched and the resulting fracture dataset is used for detailed fracture flow simulations. The resulting hydraulic aperture is calibrated with laboratory measurements using a handheld air permeameter. The air permeameter data provide a hydraulic aperture of 81 ± 1 µm. For calibration, mechanical aperture fields are calculated using stepwise increasing contact areas up to 15%. At 5% contact area, the average hydraulic aperture obtained by MLS (85 µm) is close to the measurement. For SfM, the measurements are fitted at 7% contact area (83 µm). The flow simulations reveal preferential flow through major channels that are structurally and geometrically predefined. Thus, this study illustrates that resolution and accuracy of the imaging device strongly affect the quality of fluid flow simulations and that SfM provides a promising low-cost method for fracture imaging on cores or even outcrops. Highlights: We evaluate a handheld laser scanner, a mounted laser scanner and the Structure from Motion method for imaging of fracture surfaces. We use the scanned geometries for flow simulations and calibrate the results with air permeameter measurements. Our study reveals that the handheld laser scanner is unsuitable for fracture surface imaging due to low resolution and accuracy. The simulations performed with geometries obtained by the mounted laser scanner and the Structure from Motion method provided comparable results. The Structure from Motion method seems to be promising for fracture investigations in field applications due to the high flexibility and low costs. [ABSTRACT FROM AUTHOR]

Rapid, precise and frugal surveying is a strategic factor in the present mechanized mining environment owing to rapid surface profile transformation in a short period. Determination of global coordinates with high accuracy using the satellite-based global navigation satellite system (GNSS) has been studied in the mining arena and is gradually becoming an essential component. A case study of Khanak Stone Mine, Haryana, India, is discussed here, highlighting the combination of GNSS system and terrestrial laser scanner for the preparation of a surface profile of a rugged terrain with the key objective to determine the precise in situ rock excavation in periodical phases. The actual tonnage of rock produced/transported from the mine has been calculated by determining the in situ density and bulk density in the laboratory. The study reveals that this survey of approach is apt to achieve quality plan and to obtain the goal of actual tonnage of rocks excavated. [ABSTRACT FROM AUTHOR]

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve. [ABSTRACT FROM AUTHOR]

In large-scale poultry farming, real-time online measurement of egg weight and shape parameters remains a challenge. To address this, we developed FEgg3D, a non-contact dynamic measuring device based on a self-designed laser scanner. The device employed a subset of the point cloud generated to predict the shape parameters and weight of eggs using machine learning algorithms. Different colors and sizes of eggs on various backgrounds were scanned using FEgg3D mounted on a gantry system. Our results demonstrated the following: (1) The Support Vector Regression (SVR) model was optimal for major axis length estimation, with an R2 of 0.932 using six laser lines and eight points per line. (2) The Gaussian Process Regression (GPR) model excelled in minor axis length estimation, achieving an R2 of 0.974 with six laser lines and 16 points per line. (3) SVR was optimal for volume estimation, attaining an R2 of 0.962 with six laser lines and 16 points per line. (4) GPR showed superior performance in weight prediction, with an R2 of 0.964 using five laser lines and 16 points per line. Including density features significantly improved accuracy to an R2 of 0.978. This approach paves the way for advanced online egg measurement in commercial settings.

The church of San Pedro, located in Becerril de Campos (Palencia), stands as a monumental temple of relevant heritage value. The architectural rehabilitation intervention that was carried out required the writing of a complete and exhaustive documentation of the state of the temple. Therefore, the initial objective was to have an accurate representation of its geometry and, above all, a quantification of the deterioration and alterations that its architectural structures presented. Digital models, planimetries and photogrammetric restorations were necessary to locate and evaluate the extent of damage to the building. This prior action was essential to substantiate the hypotheses about the origin of their pathologies and, subsequently, establish solutions. To satisfy the analytical demands, a mixed precision methodology was developed, using the combination of a 3D laser scanner and terrestrial and aerial digital photogrammetry. [ABSTRACT FROM AUTHOR]

A new segmentation and topology denoising method is proposed wherein the laser scanning three-dimensional (3-D) point cloud data and datum points are extracted. The laser scanned 3-D point cloud model is cut in the horizontal plane. The minimum distance between the point cloud data on the horizontal cutting plane and the geodesic line of the datum point is the feature points. The skeleton of the 3-D point cloud model is constituted by the feature points and the point cloud data are segmented according to the skeleton feature points. In the 3-D space of laser scanning point cloud data the original point cloud is transformed in two-dimensional (2-D) space by the spatial topological mapping function. The distance weight is computed by segmented point cloud data. In the process of topological transformation, combining with the distance weight of the sampling point, the topology transformation function is built. The noise point and the non-noise point can be separated by the topology transformation. The large-scale noise points are removed by comparison with a set threshold. The fairing direction of small scale noise is calculated according to the curvature and point cloud density. The small scale noise point is moved along the fairing direction to achieve the effect of denoising of the point cloud data. The method is verified by the point cloud data of laser scanning ring forgings in this paper. [ABSTRACT FROM AUTHOR]

As climate changes, maintenance of yield stability requires efficient selection for drought tolerance. Drought-tolerant cultivars have been successfully but slowly bred by yield-based selection in arid environments. Marker-assisted selection accelerates breeding but is less effective for polygenic traits. Therefore, we investigated a selection based on phenotypic markers derived from automatic phenotyping systems. Our trial comprised 64 potato genotypes previously characterised for drought tolerance in ten trials representing Central European drought stress scenarios. In two trials, an automobile LIDAR system continuously monitored shoot development under optimal (C) and reduced (S) water supply. Six 3D images per day provided time courses of plant height (PH), leaf area (A3D), projected leaf area (A2D) and leaf angle (LA). The evaluation workflow employed logistic regression to estimate initial slope (k), inflection point (Tm) and maximum (Mx) for the growth curves of PH and A2D. Genotype × environment interaction affected all parameters significantly. Tm(A2D)s and Mx(A2D)s correlated significantly positive with drought tolerance, and Mx(PH)s correlated negatively. Drought tolerance was not associated with LAc, but correlated significantly with the LAs during late night and at dawn. Drought-tolerant genotypes had a lower LAs than drought-sensitive genotypes, thus resembling unstressed plants. The decision tree model selected Tm(A2D)s and Mx(PH)c as the most important parameters for tolerance class prediction. The model predicted sensitive genotypes more reliably than tolerant genotype and may thus complement the previously published model based on leaf metabolites/transcripts. [ABSTRACT FROM AUTHOR]

Currently, digital technologies provide engineers with the ability to improve quality control in civil engineering and construction. In this industry, structural quality control aims to prevent deviations that may occur in the assembly of the structure, to guarantee compliance with the essential requirements for its operation. In this context, an attempt was made to learn whether the application of digital technologies, particularly use of the laser scanners, is a resource that facilitates quality control procedures, making them digital and more reliable. In the presented case study, the methodology involved the use of a laser scanner in the quality control of the assembly of a steel structure of an industrial building. Thus, after the study on technologies associated with the automation of geometric object checks, with the use of the computational tools and digital laser survey technologies, the structural elements assembled in situ were analyzed. The main results this study allow us to conclude that the use of the digital laser scanner survey technologies contributes favorably to the automation of geometric quality control procedures in the assembly of steel structures. [ABSTRACT FROM AUTHOR]

O registro de nuvens de pontos 3D e o refinamento global de poses são dois problemas fundamentais ao realizar o Mapeamento e Localização Simultâneos (Simultaneous Localization and Mapping - SLAM) com sensores LIDAR. O registro de nuvens consiste em encontrar transformações de coordenadas que sobrepõem localmente pares de nuvens de pontos, chamadas poses relativas. Para referenciar várias nuvens em uma origem global, várias poses relativas precisam ser compostas multiplicativamente em poses absolutas ao longo da trajetória do sensor, como as poses relativas nunca estão isentas de erros, um problema ainda mais geral surge, a deriva (drift) da trajetória do sensor. Para tratar este problema se utilizam Modelos de Refinamento Global (MRG), que refinam simultaneamente todas as poses de uma trajetória. Neste contexto, propõe-se aqui duas contribuições, a primeira, é um método de registro de pares de nuvens de pontos que integra o Fast Global Registration (FGR) e o Generalized Iterative Closest Point (GICP) em uma abordagem multicaminho e em multiescala. Para isto, cada nuvem de um dataset é registrada nas 3 posteriores, criando um grafo de poses, e cada par é sucessivamente registrado em abordagem coarse-to-fine. A segunda contribuição, se trata de um MRG linear e fechado capaz de refinar todas as poses de um circuito, sem necessidade de iterações ou definição de parâmetros. Para isto, as rotações das poses são mapeadas em quatérnios e interpoladas por meio da técnica Spherical Linear Interpolation (SLERP). Em seguida outra otimização linear baseada no modelo LUM-3D é aplicada. A combinação dos modelos foi testada em dois datasets distintos, um com sete nuvens de pontos obtidas por Laser Scanner Terrestre (LST) e outro com 901 nuvens obtidas por Laser Scaner Móvel (LSM). Em ambos, os modelos foram capazes de reconstruir totalmente os datasets e reduzir significativamente os erros de registro e deriva.

The research deals with the topic of archaeological surveys through the use of laser scanners and drones and the integration of appropriate data processing methodologies, aimed at promoting processes of documentation, knowledge, restoration and enhancement of the archaeological heritage.The object of the investigation is the Mausoleum of Sant’Urbano at the 4th mile of the Via Appia Antica, dating back to the 4th century AD, annexed to the Appia Antica Archaeological Park in 2021. Prior to 2021 it was privately owned and had undergone disfigurements and alterations of the original masonry.The research presented derives from a scientific collaboration agreement between the Appia Antica Archaeological Park, the Department of Architecture at Roma Tre University, the Department of Humanities, Philosophical Studies, and History of Art at Tor Vergata University, aimed at study, survey and monumental analysis of the mausoleum.The presented research resulted in the definition of a survey methodology of the archaeological heritage in which the data acquisitions from the aerial photogrammetric method and from the instrumental method are integrated aimed at the documentation, knowledge and dimensional and morphological restitution of an asset of significant historical and identity interest. The first results have made it possible to obtain a graphic restitution, numerical models or orthophotos on the basis of which the archaeological and historical multidisciplinary studies, the restoration and conservation interventions and the enhancement and use strategies by the Appia Antica Archaeological Park will be conducted.DOI: https://doi.org/10.20365/disegnarecon.30.2023.4

The architectural graphic expression, with all the variants that make it up, strengthens a substantial part of the process of approaching the project, also of its resolution. When the architectural intervention corresponds to the rehabilitation of a historic building, the aforementioned link acquires greater prominence, both in the previous phases of analysis, and in the consequent decisions that will derive from them. The help of technologies in the capture of information and the possibilities of virtualization provided by computer applications, have determined both the work methodologies and the forms of graphic representation to obtain results.This article describes the processes carried out for the graphic representation of a Renaissance palace in the province of Teruel, Casa Castellote, as a starting point to advance in its knowledge.Every graphic survey, any virtual model analyzing an architectural object, the intangible contribution of the initial sketches, and the conclusive planimetric representation configure an essential scientific research to address a project solution.This analytical graphic process, as well as the consequent creative graphic process, require systematization. The result is the obtaining of a digital model that will allow the study and analysis of the model through different visualizations, whether planimetry, or virtual three-dimensional models, which, in addition, enable accessibility to the documentation of the heritage asset and its dissemination to society.DOI: https://doi.org/10.20365/disegnarecon.30.2023.9

The castle of Ademuz constitutes a relevant heritage value of this spanish town and of the Rincón de Ademuz municipal district and is a key element to understand the configuration of the village and its importance as a defensive town throughout history. However, the lack of documents about the castle and its lamentable condition make it a largely unknown monument. Actions are currently implemented to reverse this alarming situation and the drafting of the Master Plan of the Castle of Ademuz has been the first step to raise awareness about its recovery and enhancement. The Master Plan has summarized the existing documentary information about the castle and its legal and urban situation as a step prior to its constructive and formal analysis. The graphic survey carried out in the drafting of the Master Plan constitutes an unpublished knowledge contribution on this property, which has allowed us to establish hypotheses about its compositional traces and to guide the lines of action for its recovery.This unprecedented graphic survey will also allow defining each of the lines of action of the Master Plan, from the structural consolidation of the remains of the castle to the conditioning of the spaces that surround it, as well as its cultural dissemination through the new digital technologies linked to virtual reality and augmented reality, among others. The objective is to enhance this architectural heritage through its knowledge and dissemination, so that this legacy is preserved for future generations.DOI: https://doi.org/10.20365/disegnarecon.30.2023.26

To overcome incomplete point cloud data obtained from laser scanners scanning complex surfaces, multi-viewpoint cloud data needs to be aligned for use. A hybrid improved SAC-IA with a KD-ICP algorithm is proposed for local point cloud alignment optimization. The scanned point cloud data is preprocessed with statistical filtering, as well as uniform down-sampling. The sampling consistency initial alignment (SAC-IA) algorithm is improved by introducing a dissimilarity vector for point cloud initial alignment. In addition, the iterative closest point (ICP) algorithm is improved by incorporating bidirectional KD-tree to form the KD-ICP algorithm for fine point cloud alignment. Finally, the algorithms are compared in terms of runtime and alignment accuracy. The implementation of the algorithms is based on the Visual Studio 2013 software configurating point cloud library environment for testing experiments and practical experiments. The overall alignment method can be 40%~50% faster in terms of running speed. The improved SAC-IA algorithm provides better transformed poses, combined with the KD-ICP algorithm to select the corresponding nearest neighbor pairs, which improves the accuracy, as well as the applicability of the alignment.

When evaluating the shear strength of rock mass discontinuities, certain challenges arise due to the difficulty in quantifying the roughness characteristics of surfaces and the strength of asperities. Recent research has focused on enhancing techniques for assessing these characteristics and exploring the application of laser scanning to aid in evaluating discontinuity features. The analysis of reflectivity values (I) obtained through a laser scanner survey presents an efficient method for assessing mechanical characteristics, such as joint compressive strength (JCS). Reflectivity measurements demonstrate correlations with Schmidt hammer rebound values (r). The laser scanner technique would enable the measurement of JCS without the direct application of the Schmidt hammer on rocks in areas where rebound values (r) measurements are unavailable. The use of a laser scanner allows for the acquisition of high-precision geometrical information concerning the 3D roughness and anisotropy of rock surfaces. In this study, an innovative technique was introduced that utilizes laser scanner data from six previous experimental surveys conducted on rock formations in Southern Italy. This technique facilitates the evaluation of roughness profiles, considering potential variations along kinematically admissible sliding directions, allowing for the estimation of the Joint Roughness Coefficient (JRC). This new methodology aids in evaluating the parameters of Barton’s equation to determine the strength characteristics of rock mass discontinuities.

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.

(1) The early assessment of wood quality, even while trees are standing, provides significant benefits for forest management, sales efficiency, and market diversification. Its definition cannot be in absolute terms but must always be linked to the material’s intended use. (2) In this contribution, a review of the scientific literature is given to discuss the visually evaluable attributes that define wood quality in standing trees, the applicability of the techniques used for their assessment, and the effectiveness of these attributes and technologies in predicting quality, to finally highlight future research needs. (3) The visual characteristics generally used to evaluate wood quality are linked to stem form and dimension, branchiness, and stem damage, but their assessment is challenging due to time and resource constraints. To address these challenges, laser-based and image-based techniques have been applied in field surveys. (4) Laser scanners offer detailed and accurate measurements. Photogrammetry, utilizing images to reconstruct 3D models, provides a cost-effective and user-friendly alternative. Studies have demonstrated the effectiveness of these tools in surveying the visible properties of stems and branches, but further development is necessary for widespread application, particularly in software development, with faster and more effective algorithmic advancements for automatic recognition and subsequent measurement of pertinent characteristics being critical for enhancing tool usability. (5) However, predicting wood quality from these surveys remains challenging, with a limited correlation between the visible tree characteristics assessed and the sawn product quality. Empirical studies evaluating products downstream in the forest-wood supply chain could provide valuable insights. In this sense, the implementation of traceability systems could facilitate the linkage between data on standing trees and the quality of the sawn product. Also, further research is needed to develop models that can accurately predict internal tree characteristics and their impact on product quality.