Objective: To evaluate the biomechanical effects of varying posterior implant inclinations and loading protocols on peri-implant stress distribution in full-arch maxillary rehabilitations using the All-on-Four concept. Methodology: A three-dimensional finite element model of an edentulous atrophic maxilla was developed from a digital point cloud. Four implants were placed according to the All-on-Four protocol: two anterior vertical implants and two posterior implants with inclinations of 0°, 15°, 30°, or 45°. Mini-abutments and a titanium bar prosthesis were included. Material properties were assumed as homogeneous, isotropic, and linearly elastic. Immediate loading was simulated using frictional contacts (µ = 0.3), whereas delayed loading assumed complete osseointegration (bonded contacts). The models were meshed using 10-node quadratic tetrahedral elements (SOLID187) in ANSYS®. Maximum von Mises stress in cortical bone, cancellous bone, implants, abutments, and the prosthetic bar was assessed. Results: Posterior implant tilt significantly reduced peri-implant stress. Under immediate loading, the highest stress occurred at 0° inclination in the posterior left implant (82.36 MPa) and decreased progressively with increasing tilt, reaching 33.63 MPa at 45° (≈59% reduction). Delayed loading generally produces lower stress magnitudes, particularly at extreme tilts. Anterior implants experienced lower stress levels across all configurations. Comparative analysis demonstrated that immediate loading increased stress at lower angulations, while differences between loading protocols were minimal at higher inclinations. Conclusions: Posterior implant angulation and loading protocol critically influence peri-implant stress distribution. Increased posterior tilt combined with appropriate loading reduces peak cortical bone stresses, supporting biomechanical optimization in All-on-Four maxillary rehabilitations.

Construction sites are particularly susceptible to the effects of extreme weather, with unsafe items posing a significant risk of causing substantial damage to construction projects and neighboring communities. Furthermore, data regarding the materials, machinery, and buildings present at the site are frequently obtained through manual inspection or on-site photography before the advent of extreme weather conditions. This process is resource-intensive and time-consuming. The core innovation of this study lies in the integration of digital twin technology with RandLA-Net-based point cloud semantic segmentation, optimized specifically for construction site safety management under extreme weather conditions. To achieve systematic disaster preparedness for construction sites, this study explores the potential of utilizing three-dimensional (3D) point cloud technology in construction site management. This involves acquiring location information about materials and machinery on construction sites through the development of a construction site point cloud identification system. This system is designed to identify and analyze potential risk factors in the digital twin model of a construction site, thereby optimizing the site layout at all stages. Furthermore, it enables practitioners to rapidly identify, locate, and assess potential risk factors on-site, facilitating the prompt and effective implementation of measures to prevent extreme weather.

The old and notable trees (OANTs) in classical gardens serve as key connectors to a city’s historical narrative by offering insights for sustainable conservation strategies. The incorporation of digital technology in analysing and safeguarding OANTs within these gardens is instrumental for their broader urban protection. Here, terrestrial laser scanning and panoramic photography are utilised to digitally collect OANTs in Du Fu Thatched Cottage, and obtain the digital point cloud, plant species, and OANTs quantity. Based on point cloud data processing methods, reveal the distribution characteristics, survival status, volume differences under different planting configurations, and scale control secrets in the landscape environment of the OANTs in Du Fu Thatched Cottage. Finally, a comprehensive digital conservation strategy framework is proposed with OANTs in classical gardens as the ontological core. This study facilitates the sustainable administration and conservation of OANTs in classical gardens.

While wide-area 3D Gaussian Splatting (3DGS) shows promise for scene restoration, current implementations face several challenges. There are few examples of its use, and existing 3DGS tools often have limitations when used on mobile devices, including a restricted restoration range, large data volumes, and lower accuracy. To address these issues, this study employed RealityCapture to align digital SLR camera images and point cloud data acquired from a terrestrial laser scanner. By leveraging these processed results within Postshot, a tool capable of large-scale, GUI-based 3DGS processing, our team successfully and accurately restored the vast Metasequoia Plaza at the Setagaya Campus of Tokyo University of Agriculture.

Pottery is one of the most common and abundant types of human remains found in archaeological contexts. The analysis of archaeological pottery involves the reconstruction of pottery vessels from their sherds, which represents a laborious and repetitive task. In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. In that regard, given a sherd’s digital point cloud in a standard, so-called canonical position, the proposed method predicts the geometric transformation, which moves the sherd to its expected normalized position relative to the vessel’s coordinate system. Among the main components of the proposed method, a pair of deep 1D convolutional neural networks trained to predict the 3D Euclidean transformation parameters stands out. Herein, rotation and translation components are treated as independent problems, so while the first network is dedicated to predicting translation moments, the other infers the rotation parameters. In practical applications, once a vessel’s shape is identified, the networks can be trained to predict the target transformation parameter values. Thus, given a 3D model of a complete vessel, it may be virtually broken down countless times for the production of sufficient data to meet deep neural network training demands. In addition to overcoming the scarcity of real sherd data, given a virtual sherd in its original position, that procedure provides paired canonical and normalized point clouds, as well as the target Euclidean transformation. The herein proposed 1D convolutional neural network architecture, the so-called PotNet, was inspired by the PointNet architecture. While PointNet was motivated by 3D point cloud classification and segmentation applications, PotNet was designed to perform non-linear regressions. The method is able to provide an initial estimate for the correct position of a sherd, reducing the complexity of the problem of fitting candidate pairs of sherds, which could be then carried out by a classical adjustment method like ICP, for instance. Experiments using three distinct real vessels were carried out, and the reported results suggest that the proposed method can be successfully used for aiding pottery reconstruction.

Due to the imbalance between urban and rural development and improper management, the spatial forms of many heritage villages have suffered severe damage, and their landscape styles are gradually being blurred, posing serious challenges to the protection of traditional villages. Taking the traditional village of Xi Songbi in Jiexiu City, Shanxi Province, as a case study, this paper employs UAV low-altitude multi-view measurement technology to obtain high-resolution image data from different angles. Three-dimensional modeling technology is then used to construct a 3D real-world model, orthophotos, and point cloud data of the settlement. Based on these data, the weakly supervised point cloud segmentation method, DDLA, is further applied to finely segment and classify the acquired point cloud data, accurately extracting key spatial elements such as buildings, roads, and vegetation, thereby enabling a comprehensive and quantitative analysis of the spatial morphology of traditional villages. The results of the study show the following: (1) The use of UAVs for low-altitude multi-view measurement not only greatly improves the efficiency of data acquisition but also provides millimeter-level precision spatial data in a short time through the constructed 3D models and orthophotos. (2) The acquired point cloud data can be processed through the DDLA, which effectively differentiates building contours from other environmental elements. (3) The calculation and analysis of the segmented point cloud data can accurately quantify key spatial morphology elements, such as the dimensions of traditional village buildings, spacing, and road widths, ensuring the scientific rigor and reliability of the data. (4) The comprehensive application of digital technology and point cloud segmentation methods provides clear expectations and solid technical support for the quantitative study of the spatial morphology of traditional villages, laying a scientific foundation for the protection and sustainable development of cultural heritage.

Purpose The study aims to apply reality capture technology to enhance the cost valuation and work verification process of work completed. Construction work completed during the execution phase must be evaluated periodically to ascertain the contractor’s financial resources invested for payment. The manual process is cumbersome and time-consuming, subjective due to human intervention and often results in assumptions complicating accurate and efficient project cost management. Claim disputes and delayed payment usually arise due to a lack of clarity on the value of work completed. Design/methodology/approach This study used proof of concept on an ongoing construction project to assess the valuation process of work done by using a reality capture (RC) technology – a drone for the acquisition of point cloud data for measurement extraction and valuation of work completed. Works verification was carried out using the as-design digital and as-built point cloud models. Findings The valuation process becomes dependable, transparent, accurate and efficient using RC technology, which can offer detailed digital data for visual inspection and assessment by all stakeholders. Extraction of quantities of work completed for cost valuation is done using the as-built point clouds obtained from aerial imagery captured by drones. Originality/value The study presents a practical solution by integrating RC technology into the cost valuation workflow to increase the correctness, transparency, efficiency and effective process for cost valuation, verification and payment certification. Using the potential of RC technology has enhanced communication, collaboration and coordination among the project stakeholders for seamless and timely payment approvals. This lessens the time for contractors’ claim approval since evidence of work done can be readily accessible remotely.

Under the tide of modern social development, the conservation and development of ancient villages face numerous challenges. Melang Village, a representative traditional village in Hainan, holds significant historical and cultural value. This study focuses on Melang Village, employing modern technologies such as field surveys and digital point cloud data collection to analyze its architectural forms and surrounding environmental resources. Based on this, a revitalization and adaptive reuse design plan for immovable cultural heritage buildings is proposed. the research aims to provide scientific guidance for the sustainable development of Melang Village and serve as a reference for the conservation and development of other ancient villages.

Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses. The DSM accurately captures terrain features and elevation changes, offering a comprehensive view of the land surface. Conversely, LiDAR point cloud data delivers detailed three-dimensional information, enabling precise mapping of terrain features and object detection. Our comparative analysis based on six selected locations with varied topographical arrangements considers factors such as visual acuity and computational efficiency. Additionally, we discuss the application of DSM and LiDAR point cloud data in view analysis, emphasizing their value in line-of-sight assessments and field operations. The results indicate greater precision of viewsheds created based on LiDAR point clouds. The analysis reveals that the greater precision in comparing differences between DSM and point LiDAR data ranges from 1.42% to 5.94%, while the results subtraction falls between 1.05% and 3.89% for the conditions analyzed, indicating a high degree of accuracy in the method. However, this process demands significant computational resources. It is best applied in limited areas, particularly in urban environments where such data is crucial for supporting research decisions.

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R2 = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops.

Abstract Ho Chi Minh City, the largest urban agglomeration in Vietnam, is at the forefront of enabling the government’s efforts and advocating for smart city initiatives in the coming decades. To facilitate this advancement, the implementation of solutions to support the collection, construction, and updating of spatial databases is crucial. These databases serve as the foundational platform for effective urban management practices. Unmanned Aerial Vehicles (UAVs) have witnessed significant growth across various domains, particularly in spatial data collection. However, challenges arise when acquiring and processing UAV images in urban areas. These challenges include orthomosaic distortion in certain regions, insufficient accuracy for creating extremely large-scale maps (e.g., 1:200 or 1:500), and unclear boundaries between adjacent objects. The objective of this paper is to present flight tests conducted at varying altitudes, with different shooting angles, and varying layouts of Ground Control Points (GCPs), both with and without the UAV’s RTK receiver mode. In order to improve the accuracy of UAV’s images, the results suggest that the precision of orthomosaic, Digital Surface Model (DSM) and point clouds in the context of 3D mapping depends on various factors, including: the determination of the optimal flight altitude, optimizing number of GCPs, selecting an appropriate level of image processing, capturing images from varied angles, and using UAVs with RTK or PPK data acquisition modes to mitigate image distortion.

ABSTRACT Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier’s results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation.

Abstract. Airborne laser scanning (ALS) is able to penetrate sparse vegetation to obtain highly accurate height information on the ground surface. LiDAR point cloud filtering is an important prerequisite for downstream tasks such as digital terrain model (DTM) extraction and point cloud classification. Aiming at the problem that existing LiDAR point cloud filtering algorithms are prone to errors in complex terrain environments, an ALS point cloud filtering method based on supervoxel ground saliency (SGSF) is proposed in this paper. Firstly, a boundary-preserving TBBP supervoxel algorithm is utilized to perform supervoxel segmentation of ALS point clouds, and multi-directional scanning strip delineation and ground saliency computation are carried out for the clusters of supervoxel point clouds. Subsequently, the energy function is constructed by introducing the ground saliency and the optimal filtering plane of the supervoxel is solved using the semi-global optimization idea to realize the effective distinction between ground and non-ground points. Experimental results on the ALS point cloud filtering dataset openGF indicate that, compared to state-of-the-art surface-based filtering methods, the SGSF algorithm achieves the highest average values across various terrain conditions for multiple evaluation metrics. It also addresses the issue of recessed structures in buildings being prone to misclassification as ground points.

Deep learning-based 3D digital damage map of vertical-type tunnels using unmanned fusion data scanning

This paper describes the results of a field investigation with the objective of evaluating the possibility to produce drone-derived 3D digital point clouds sufficiently dense and accurate to determine the rock mass rating (RMR) in the underground mine in Bukit Kachi. Agisoft Photoscan software was used for producing the three-dimensional points cloud from the two-dimensional images' sequences. The rock mass rating was evaluated by using Discontinuity Set Extractor (DSE) and Dips 7.0 in Rockscience software. Results from this research show that 3D digital point clouds, derived from the processing of drone-flight images, were successfully used for reliable representation of discontinuity of the tunnel. According to the results of the analysis, both Tunnel A and Tunnel B are classified as "fair rock." Meanwhile, Tunnel A is failing due to geological conditions of feldspar decomposition that are classified as class I, which is "very poor rock." According to Dips 7.0 analysis, the major direction of discontinuity set of Tunnel A for Window right is N300 – N310, while for Window left is N350 – N360. The most dominant discontinuity direction for Tunnel B Window right is N340 – N350, and the orientation for Window left is N10 – N20. When manual mapping and DSE analysis are compared, both orientations of the discontinuity do not give the same direction due to less data reading in the field and high accuracy from the software.

PurposeDigital data acquisition is crucial for operations in the digital transformation era. Reality capture (RC) has made an immeasurable contribution to various fields, especially in the built environment. This paper aims to review RC applications, potentials, limitations and the extent to which RC can be adopted for cost monitoring of construction projects.Design/methodology/approachA mixed-method approach, using Bibliometric analysis and the PRISMA framework, was used to review and analyse 112 peer-reviewed journal articles from the Scopus and Web of Science databases.FindingsThe study reveals RC has been applied in various areas in the built environment, but health and safety, cost and labour productivity monitoring have received little or no attention. It is proposed that RC can significantly support cost monitoring owing to its ability to acquire accurate and quick digital as-built 3D point cloud data, which contains rich measurement points for the valuation of work done.Research limitations/implicationsThe study’s conclusions are based only on the Scopus and Web of Science data sets. Only English language documents were approved, whereas others may be in other languages. The research is a non-validation of findings using empirical data to confirm the data obtained from RC literature.Practical implicationsThis paper highlights the importance of RC for cost monitoring in construction projects, filling knowledge gaps and enhancing project outcomes.Social implicationsThe implementation of RC in the era of the digital revolution has the potential to improve project delivery around the world today. Every project’s success is largely determined by the availability of precise and detailed digital data. RC applications have pushed for more sustainable design, construction and operations in the built environment.Originality/valueThe study has given research trends on the extent of RC applications, potentials, limitations and future directions.

Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.

Abstract The rapid acquisition of 3D data with high accuracy and efficiency, along with the reduction of production cycles and costs, are pressing challenges in the field of 3D reality modeling. This paper introduces a novel approach for urban 3D real-view modeling from a metacosmic perspective. Utilizing tilt-shift photography technology, this method captures three-dimensional data of urban scenes under the meta-universe framework. The data are then processed through an integration principle, combining DOM (Digital Orthophoto Map), DEM (Digital Elevation Model), and vector data to filter out interference. Subsequently, a high-density digital point cloud is generated using tilted imagery combined with aerial triangulation. This point cloud, along with a TIN (Triangulated Irregular Network) model, facilitates the construction of a comprehensive three-dimensional visualization model of urban environments, enabling detailed digital analysis. Data presentation demonstrates that with an increase in the number of image control points, the planimetric error decreases from 0.0536 m to 0.0388 m, reflecting a 27.61% improvement. Similarly, elevation accuracy improves from 0.0927 m to 0.0539 m, marking a 41.86% enhancement. This methodology supports the creation of highly precise and cost-effective three-dimensional realistic models of urban built-up areas, providing robust data support for the development and management of smart cities.

Quantitative measuring of gravity erosion contributes to a better understanding of soil-mass failure occurrence and prediction. However, the measurement of gravity erosion requires the continuous monitoring of the objective terrain, due to its occurrence, usually within seconds, and combination with hydraulic erosion. The photogrammetric technique can quickly obtain terrain data and provide a new method for measuring gravity erosion. Based on a continuous high-overlapping image-acquisition equipment, a Structure-from-Motion-Multi-View-Stereo (SfM-MVS)-integrated workflow, and volume calculation, a new working methodology was established for measuring gravity erosion on steep granitic slopes in the laboratory. The results showed a good match between the digital point clouds derived from SfM-MVS-integrated workflow and terrestrial laser scanning (TLS), achieving millimeter-scale accuracy. The mean distance between the point clouds derived from TLS and SfM-MVS was 1.13 mm, with a standard deviation of 0.93 mm. The relative errors among the volumes calculated by SfM-MVS and TLS or the conventional oven-drying method were all within 10%, with a maximum error of 9.3% and a minimum error of 0.2%. A total of 213 gravitational erosion events were measured in the laboratory by using the SfM-MVS method, further confirming its feasibility.

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.