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At present, there are diverse data sources and mature technologies for the 3D modeling of cities, and 3D modeling is also developing towards component-level and refined modeling. In this study, we combined the needs of refined management in the central area of Beijing and elaborated on the practice of using various point cloud collection devices to carry out multisource point cloud data collection and ultrafine 3D modeling in areas where flight operations are not possible. There are three main areas of innovation: (1) the development of full-element ground acquisition and multisource point cloud joint 3D modeling technology, which solves the technical problems of acquiring all types of ground scanning data, such as those obtained using static, station-, cart-, backpack-, and vehicle-mounted laser scanning instruments, and handheld scanners, as well as multisource point cloud data fusion and modeling; (2) the development of a vehicle-mounted ultrahigh-resolution image acquisition system, which improves the efficiency of the vehicle scanner by increasing the exposure frequency to enhance its travel speed and (3) the use of tools to automatically extract urban components based on point clouds, which improved the efficiency of 3D modeling and achieved the fine-grained modeling of massive components in the region. On the basis of the system platform, the management and implementation of urban component-level information can be achieved. [ABSTRACT FROM AUTHOR]

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method. [ABSTRACT FROM AUTHOR]

The rapid growth in technologies for 3D sensors has made point cloud data increasingly available in different applications such as autonomous driving, robotics, and virtual and augmented reality. This raises a growing need for deep learning methods to process the data. Point clouds are difficult to be used directly as inputs in several deep learning techniques. The difficulty is raised by the unstructured and unordered nature of the point cloud data. So, machine learning models built for images or videos cannot be used directly on point cloud data. Although the research in the field of point clouds has gained high attention and different methods have been developed over the decade, very few research works directly with point cloud data, and most of them convert the point cloud data into 2D images or voxels by performing some pre-processing that causes information loss. Methods that directly work on point clouds are in the early stage and this affects the performance and accuracy of the models. Advanced techniques in classical convolutional neural networks, such as the attention mechanism, need to be transferred to the methods directly working with point clouds. In this research, an attention mechanism is proposed to be added to deep convolutional neural networks that process point clouds directly. The attention module was proposed based on specific pooling operations which are designed to be applied directly to point clouds to extract vital information from the point clouds. Segmentation of the ShapeNet dataset was performed to evaluate the method. The mean intersection over union (mIoU) score of the proposed framework was increased after applying the attention method compared to a base state-of-the-art framework that does not have the attention mechanism. [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.)

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.

In large open-pit coal mine projects, internal dump slope failure is a serious issue for industry. Slope failure affects haul roads, mine machinery, and miners' lives and hampers coal production operations. Dump slope failure events will not occur suddenly, and progressive displacement will occur in the slope before failure. Therefore, dump slope monitoring is essential for the safety of humans and machinery. Generally, the mine slope displacement is measured using a conventional survey approach. However, conventional surveys cannot measure minor changes in a large dump area. Therefore, massive dump displacement monitoring is a tedious task for surveyors. This study used the unmanned aerial vehicle (UAV) close-range photogrammetry survey to introduce an intelligent and reliable approach for dump change detection and displacement monitoring. A UAV close-range photogrammetry survey was conducted to collect RGB images, and dump surface point cloud data (PCD) were generated using the structure-from-motion (SfM) computing algorithm. The ground control points (GCPs) were also measured using a coordinate measurement survey for geo-reference points. For dump surface PCD and 3D reconstruction, the Pix-4D Mapper image processing tool was used. PCDs were generated for two months and used for dump slope surface change and displacement monitoring using the cloud-to-cloud (C2C) computing algorithm. The PCD was compared with the reference PCD, and the results of this investigation indicated minor and major changes on the dump slope and top surfaces. This approach is inexpensive and reliable for large dump slope change detection and displacement monitoring. [ABSTRACT FROM AUTHOR]

Building outline extraction from segmented point clouds is a critical step of building footprint generation. Existing methods for this task are often based on the convex hull and α-shape algorithm. There are also some methods using grids and Delaunay triangulation. The common challenge of these methods is the determination of proper parameters. While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection, their reliance on datasets with ground truth information limits the generalization of these methods. In this study, a novel unsupervised approach, called PH-shape, is proposed to address the aforementioned challenge. The methods of Persistence Homology (PH) and Fourier descriptor are introduced into the task of building outline extraction. The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius, thus enabling the parameter-adaptive extraction of building outlines through buffering and “inverse” buffering. The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types, interior boundaries, and the density variation in the point cloud data of one building. The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.

Mobile robots' efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms. [ABSTRACT FROM AUTHOR]

Building outline extraction from segmented point clouds is a critical step of building footprint generation. Existing methods for this task are often based on the convex hull and α-shape algorithm. There are also some methods using grids and Delaunay triangulation. The common challenge of these methods is the determination of proper parameters. While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection, their reliance on datasets with ground truth information limits the generalization of these methods. In this study, a novel unsupervised approach, called PH-shape, is proposed to address the aforementioned challenge. The methods of Persistence Homology (PH) and Fourier descriptor are introduced into the task of building outline extraction. The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius, thus enabling the parameter-adaptive extraction of building outlines through buffering and "inverse" buffering. The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types, interior boundaries, and the density variation in the point cloud data of one building. The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines. [ABSTRACT FROM AUTHOR]

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems. [ABSTRACT FROM AUTHOR]

In this study, point cloud data were obtained from an underground limestone mine using a laser scanner. A numerical analysis was performed using discontinuous and continuous models, and the stability of two pillars located in large underground openings was analyzed. The safety factor was 1.0 or higher in both analyses, and the displacement was insignificant compared to the pillar size, confirming the pillar stability. Additionally, the safety factor of the discontinuous model was reduced by approximately 30% compared to that of the continuous model, and the displacement was increased by approximately 40%. The change in the safety factor was examined by applying the minimum, average and maximum mechanical properties of the investigated rock mass. The analysis determined that an average change of 50% occurred in the safety factor when the minimum and maximum mechanical properties of the rock mass were compared. [ABSTRACT FROM AUTHOR]

Accurate detection and analysis of cracks is critical for ensuring the safety and reliability of concrete bridges. Point cloud data (PCD) obtained from 3D scanning provides a promising avenue for automated crack assessment. However, processing the massive and unstructured PCD poses significant challenges in feature extraction and crack modeling. This paper proposes a novel method for bridge crack analysis by combining PCD feature extraction with a hierarchical neural network and Rodriguez rotation. The method first extracts crack features from PCD using outlier removal, denoising, and 3D coordinate conversion. A crack analysis model is then constructed by integrating multi-scale feature extraction and Rodriguez rotation into a hierarchical neural network, enabling the capture of both local and global crack patterns. Experiments on a benchmark data set demonstrate the effectiveness of the proposed approach, achieving 92.83% feature extraction accuracy, 95.73% parameter analysis accuracy, 93.51% recognition accuracy, and 0.91 F1 score. The method also shows improved efficiency compared to existing techniques. These results highlight the potential of the proposed PCD-based approach for accurate and efficient crack analysis in concrete bridges. [ABSTRACT FROM AUTHOR]

Safety diagnosis through the surface inspection of existing buildings is a crucial fundamental inspection. However, the assessment results often differ depending on the investigator's knowledge and experience, which poses challenges in ensuring the reliability and objectivity of the evaluation results. To address this issue, a preliminary assessment method for structural performance (PAMSP) of an RC Rahmen structure building was developed in this study to evaluate three surface survey items (cracks, horizontal deflections, and vertical deflections), which have a significant influence on the structural performance evaluation of buildings. The PAMSP can be operated based on point cloud data generated during the scan-to-building information modeling (BIM) process. Based on the results of two case studies, the PAMSP was found to be excellent in terms of accuracy, applicability, and ease of use. In conclusion, by using the PAMSP, it is expected that the fundamental structural performance of a building can be measured more objectively and reliably based on the data generated during the scan-to-BIM process, which is widely used in the digitization of existing buildings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture 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.)

The combination of reverse engineering (RE) with additive manufacturing (AM) is widely used for solid freeform fabrication and overcomes limitations with the current capabilities of designers and CAD packages that are restricted to classical model shapes. The shape flexibility offered by RE typically leads to the generated CAD models being represented in point cloud formats or stereolithography files. One resulting challenge is the difficulty with tolerance specification for the RE-generated models, especially for freeform shapes. Geometric metrology for the AM-produced parts to determine their geometric conformation can also be involved. Furthermore, conventional process analyses cannot be performed directly after RE because of the complex geometric data structure. We propose to address all these issues with a new tolerance specification and process analysis framework based on volumetric data analysis. The tolerance zone constructed under our framework, and the corresponding geometric measurements, are consistent with the profile tolerance in geometric dimensioning and tolerancing standards. Reverse engineering's process capability is assessed under our methodology via a parametric bootstrap procedure in the size-and-shape space. The performance and utility of the proposed framework are validated via a process capability study on RE-generated CAD models of multiple, additively manufactured freeform objects. [ABSTRACT FROM AUTHOR]

Harnessing the remarkable ability of the human brain to recognize and process complex data is a significant challenge for researchers, particularly in the domain of point cloud classification--a technology that aims to replicate the neural structure of the brain for spatial recognition. The initial 3D point cloud data often suffers from noise, sparsity, and disorder, making accurate classification a formidable task, especially when extracting local information features. Therefore, in this study, we propose a novel attention-based end-to- end point cloud downsampling classification method, termed as PointAS, which is an experimental algorithm designed to be adaptable to various downstream tasks. PointAS consists of two primary modules: the adaptive sampling module and the attention module. Specifically, the attention module aggregates global features with the input point cloud data, while the adaptive module extracts local features. In the point cloud classification task, our method surpasses existing downsampling methods by a significant margin, allowing for more precise extraction of edge data points to capture overall contour features accurately. The classification accuracy of PointAS consistently exceeds 80% across various sampling ratios, with a remarkable accuracy of 75.37% even at ultra-high sampling ratios. Moreover, our method exhibits robustness in experiments, maintaining classification accuracies of 72.50% or higher under different noise disturbances. Both qualitative and quantitative experiments affirm the efficacy of our approach in the sampling classification task, providing researchers with a more accurate method to identify and classify neurons, synapses, and other structures, thereby promoting a deeper understanding of the nervous system. [ABSTRACT FROM AUTHOR]

Observation of the external 3D shape/structure and some measurable phenotypic traits is of great significance to screening excellent varieties and improving crop yield in agriculture. The dense crop point clouds scanned by 3D sensors not only may include imaging noise, but also contain a large number of redundant points that will put high burden on storage and slow down the speed of algorithm for point cloud segmentation, classification, and other following processing steps. To reduce the complexity of point cloud data and meanwhile better represent the structure under limited resources, this paper presents a new Self-organizing Map (SOM)-based down-sampling strategy that is tailored for plant (or plant-like) point clouds. Our SOM-based sampling works in a purely unsupervised manner and precisely controls the number of points after down-sampling. It obtains shape-aware sampling on irregular plant point clouds by automatically encoding preliminary semantics to different organ types (e.g., stems are sampled as "lines", and leaves are sampled as folded curved shaped in "surfaces"). Extensive experiments on a multi-species plant dataset were conducted using several popular deep 3D-segmentation networks as the downstream task unit, respectively. The segmentation performance of the SOM-processed dataset outperformed several other mainstream down-sampling strategies. Our SOM strategy with 1D neuron layer can be further generalized to 2D and 3D versions, and also can be extended to a more adaptive framework that automatically picks the most suitable version of SOM for each corresponding local shape component. The proposed strategy also showed good potential in serving different applications including point cloud skeleton extraction, crop main stem length measurement; and presented satisfactory results on point cloud datasets from other domains, indicating its high applicability and good data domain adaptation. [ABSTRACT FROM AUTHOR]

Quality assurance and maintenance play a crucial role in engineering construction, as they have a significant impact on project safety. One common issue in concrete structures is the presence of defects. To enhance the automation level of concrete defect repairs, this study proposes a computer vision-based robotic system, which is based on three-dimensional (3D) printing technology to repair defects. This system integrates multiple sensors such as light detection and ranging (LiDAR) and camera. LiDAR is utilized to model concrete pipelines and obtain geometric parameters regarding their appearance. Additionally, a convolutional neural network (CNN) is employed with a depth camera to locate defects in concrete structures. Furthermore, a method for coordinate transformation is presented to convert the obtained coordinates into executable ones for a robotic arm. Finally, the feasibility of this concrete defect repair method is validated through simulation and experiments. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

Copyright of Railway Investigation & Surveying is the property of Railway Investigation & Surveying 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.)

Fusing multiple sensor perceptions, specifically LiDAR and camera, is a prevalent method for target recognition in autonomous driving systems. Traditional object detection algorithms are limited by the sparse nature of LiDAR point clouds, resulting in poor fusion performance, especially for detecting small and distant targets. In this paper, a multi-task parallel neural network based on the Transformer is constructed to simultaneously perform depth completion and object detection. The loss functions are redesigned to reduce environmental noise in depth completion, and a new fusion module is designed to enhance the network's perception of the foreground and background. The network leverages the correlation between RGB pixels for depth completion, completing the LiDAR point cloud and addressing the mismatch between sparse LiDAR features and dense pixel features. Subsequently, we extract depth map features and effectively fuse them with RGB features, fully utilizing the depth feature differences between foreground and background to enhance object detection performance, especially for challenging targets. Compared to the baseline network, improvements of 4.78%, 8.93%, and 15.54% are achieved in the difficult indicators for cars, pedestrians, and cyclists, respectively. Experimental results also demonstrate that the network achieves a speed of 38 fps, validating the efficiency and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) 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.)

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.)

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.)

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.)

With the increasing of tunnel mileage, one of the most vital problems of broad-scale structures is the intelligent identification and location of structural defects, which require massive data storage capacity and high data processing efficiency. The full-field point cloud data collected by terrestrial laser scanning (TLS) can be extracted to construct three-dimensional measured models more accurately, therefore, the fusion of TLS and the finite element method (FEM) is particularly beneficial to the deformation analysis of tunnel structures. This paper combines numerical simulation and full-field measurement to interactively verify the reliability of deformation analysis based on FEM simulation and point cloud processing. The innovation of this paper is that a three-dimensional FEM is validated through the measured point cloud data of the tunnel structure. The result of this study indicated that FEM simulation and the structural health monitoring analysis are consistent. [ABSTRACT FROM AUTHOR]

Human body recognition is widely used in smart home. The current mainstream perception modalities, i.e., camera and wearable device, are vulnerable under challenging lighting conditions and poor convenience. On the other hand, Multi-human body recognition remains as one of the most challenging tasks in a dynamic and complex environment. In this work, we introduce the low-cost multiple-input-multiple-output (MIMO) millimeter-wave radar without exposing user's private information for human body recognition in smart home. We propose a human body recognition scheme with the clustering based on the human body tracking using the sparse point cloud data of MIMO millimeter-wave radar. Firstly, the possible position of human body is predicted based on Kalman filter. Then, the point cloud data is clustered based on the human body shape in the prediction range of the human position. Finally, label tags are used to mark the human body targets detected by each frame of the radar. We apply human body recognition to validate the effectiveness of the proposed scheme. It can achieve single-person and double-person recognition using the sparse point cloud data of MIMO millimeter-wave radar. The results show that our proposed scheme reduces the error probability by 23.4% for the single-person recognition and by 31.1% for the double-person recognition. Extensive evaluations on the application of human activity recognition well demonstrate the practicability of the proposed scheme. [ABSTRACT FROM AUTHOR]

This study investigates the use of terrestrial laser scanning (TLS) in urban excavation sites, focusing on enhancing ground deformation detection by precisely identifying opening geometries, such as gaps between pavement blocks. The accuracy of TLS data, affected by equipment specifications, environmental conditions, and scanning geometry, is closely examined, especially with regard to the detection of openings between blocks. The experimental setup, employing the BLK360 scanner, aimed to mimic real-world paving situations with varied opening widths, allowing an in-depth analysis of how factors related to scan geometry, such as incidence angles and opening orientations, influence detection capabilities. Our examination of various factors and detection levels reveals the importance of the opening width and orientation in identifying block openings. We discovered the crucial role of the opening width, where larger openings facilitate detection in 2D cross-sections. The overall density of the point cloud was more significant than localized variations. Among geometric factors, the orientation of the local object geometry was more impactful than the incidence angle. Increasing the number of laser beam points within an opening did not necessarily improve detection, but beams crossing the secondary edge were vital. Our findings highlight that larger openings and greater overall point cloud densities markedly improve detection levels, whereas the orientation of local geometry is more critical than the incidence angle. The study also discusses the limitations of using a single BLK360 scanner and the subtle effects of scanning geometry on data accuracy, providing a thorough understanding of the factors that influence TLS data accuracy and reliability in monitoring urban excavations. [ABSTRACT FROM AUTHOR]

At present, three-dimensional laser scanners are used to scan subway shield tunnels and generate point cloud data as the basis for extracting a variety of information about tunnel defects. However, there are obstacles in the tunnel such as pipelines, tracks, and signaling systems that cause noise in the point cloud. Usually, the data of the tunnel point cloud are huge, and the efficiency of artificial denoising is low. Faced with this problem, based on the respective characteristics of the geometric shape and reflection intensity of the tunnel point cloud and their correlation, this paper proposes a tunnel point cloud denoising method. The method includes the following three parts: reflection intensity threshold denoising, joint shape and reflection intensity denoising, and shape denoising. Through the experiment on the single-ring segment point cloud of a shield tunnel, the method proposed in this paper takes 2 min to remove 99.77% of the noise in the point cloud. Compared with manual denoising, the method proposed in this paper takes two fifteenths of the time to achieve the same denoising effect. The method proposed in this paper meets the requirements of a tunnel point cloud data survey. Thus, it provides support for the efficient, accurate, and automatic daily maintenance and surveys of tunnels. [ABSTRACT FROM AUTHOR]

In consideration of the limited accuracy of individual tree canopy segmentation algorithms due to the diverse canopy structure and complex environments in mountainous and hilly areas, this study optimized the segmentation parameters of three algorithms for individual tree canopy segmentation of Camellia oleifera in such environments by analyzing their respective parameters. Utilizing an Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system, we obtained Canopy Height Models (CHM) of Camellia oleifera canopies based on Digital Elevation Models (DEM) and Digital Surface Models (DSM). Subsequently, we investigated the effects of CHM segmentation, point cloud clustering segmentation, and layer stacking fitting segmentation on Camellia oleifera canopies across different research areas. Additionally, combining ground survey data from forest lands with visual interpretation of UAV orthophoto images, we evaluated the performance of these three segmentation algorithms in terms of the F-score as an evaluation indicator for individual tree canopy segmentation accuracy. Combined with the Cloth Simulation Filter (CSF) filtering algorithm after removing the ground point cloud, our findings indicate that among different camellia densities and terrain environments, the point cloud clustering segmentation algorithm achieved the highest segmentation accuracy at 93%, followed by CHM segmentation at 88% and the layer stacking fitting segmentation method at 84%. By analyzing the data from UAV-LiDAR technology involving various land and Camellia oleifera planting types, we verified the applicability of these three segmentation algorithms for extracting camellia canopies. In conclusion, this study holds significant importance for accurately delineating camellia canopies within mountainous hilly environments while providing valuable insights for further research in related fields. [ABSTRACT FROM AUTHOR]

Radial composite deviation is an important index to evaluate the precision of gear transmission, and its detection method is also an important trend of gear detection in industrial development. The ability to predict radial composite deviation while obtaining geometric deviation of gears is a key challenge in advancing intelligent online inspection within gear manufacturing workshops. This study presents a virtual measurement method for determining gear radial composite deviation using point cloud data. The method involves using an optical three-dimensional scanner to capture tooth surface cloud data, determining tooth profile deviation, and applying reverse engineering technology to reconstruct the gear model, the variation curve of gear center distance is determined by simulating, allowing for the calculation of the total radial composite deviation and tooth-to-tooth radial composite deviation. In comparison to actual measurement, the virtual measurement method is generally reliable. It can effectively illustrate the overall trend of center distance variation and has good interpretability for predicting radial composite deviation results. This method reduces the dependence on the specialized gear measuring instrument and addresses the critical issue of simultaneously obtaining tooth profile deviation and radial composite deviation.

In this work, we provide an overview of the XAI (Explainable Artificial Intelligence) works related to explaining the methods working on point cloud (PC) data. The recent decade has seen a surge in artificial intelligence (AI) and machine learning (ML) algorithms finding applications in various fields dealing with a wide variety of data types such as image and text data. Point cloud data is one of these datatypes that has seen an upward trend in the use of AI/ML algorithms. However, not all these AI algorithms are “white box” models that can be understood by humans easily. Many of them are hard to interpret or understand and thus, require methods to provide explanations for the decision-making process. These methods that attempt to provide explanations or insights into the working of AI models working on various datatypes are grouped under XAI. Even though the use of datatypes such as point clouds for AI models has seen an upward trajectory, we see a lack of survey works documenting the developments in the corresponding XAI field. This issue is addressed through our contribution. We classify the literature based on different criteria such as XAI mechanism used, AI models, their tasks, type of model learning and the type of point cloud data taken into consideration. This can help readers identify works that address specific tasks and obtain corresponding details easily. We also provide useful insights regarding the surveyed papers that highlight interesting aspects of the surveyed literature.

With the continuous development of urban traffic and the improvement of intelligent transportation systems, the accurate extraction and analysis of road traffic markings becomes more and more important. At present, road traffic marking is extracted by collecting dense point cloud data with high-thread LiDAR. However, due to the high cost of the high-thread LiDAR and the large amount of point cloud data, the production efficiency of high-precision maps is low, and updating is complex and difficult. Therefore, a traffic marking extraction algorithm based on image and point cloud data fusion is proposed. The innovation of this study lies in the introduction of image processing for traffic marking edge detection, and the adaptive threshold segmentation of point cloud data using the maximum inter class variance method. The algorithm first uses an edge detection algorithm on the ground of the Canny operator to extract edge information of traffic markings from road images. Meanwhile, it uses the maximum inter class variance method to extract the internal point coordinate information of traffic markings from point cloud data. Subsequently, clustering segmentation, geometric correction and other techniques are used to process the fused data, thereby achieving automated extraction of road traffic markings. The results show that on the TuSimple dataset, the false detection rate and missed detection rate of the edge detection algorithm on the ground of the Canny operator are only 3.84% and 2.79%, respectively. Meanwhile, in the TuSimple dataset, the peak signal-to-noise ratio and structural similarity of the maximum inter class variance method were as high as 39.25% and 9.23%, respectively. In addition, the average time it takes for the algorithm proposed by the research institute to extract the edge points of a single road traffic marking is only 5.69ms. Especially, the proposed method achieved a maximum improvement of 14.07% in the accuracy of traffic marking edge detection. The method proposed by the research institute effectively reduces false positives and missed detections and can accurately and efficiently extract the edges of road traffic markings, providing a new and reliable solution for the automated extraction of road traffic markings.

In recent years, indoor mobile robots have played an increasingly important role in various home, medical, commercial, and industrial applications. However, mirror surfaces commonly found in indoor environments pose challenges to the localization and navigation of indoor mobile robots. In environments with mirror surfaces, robots may misjudge the location of obstacles owing to laser reflections, leading to accidental collisions or mission failure. In this study, a single 2D LiDAR was used to identify the location of the mirrors in the environment, and to optimize the point cloud data and occupancy grid map using the mirror locations. The main innovation is the use of the intensity information of the reflected laser beam and the inherent symmetry of mirrors for real-time detection, including polygonal mirrors and mirrors without contours. Currently, the method has been experimented with in several complex environments, the accuracy of mirror identification exceeds 97%, and effectively modified the erroneous occupancy grid map.

The loss of feature information during scale propagation in the deep learning method usually causes a big misclassification rate for many complex urban scenes. For this reason, this article presents a new deep learning method, called “Feature combination and promotion network (FCPNet).” This method consists of an end-to-end feature learning layer for obtaining multiscale depth features of point clouds, an external feature combination module for obtaining more fine-grained point cloud features, and a mutiheaded separable self-attention module for learning connections between features to obtain more globally informative features. When compared with PointNet++, the proposed FCPNet improved OA, MIOU, F1-score, and Kappa in the NPM3D dataset by 1.75%, 17.02%, 2.13%, and 0.0263, respectively. When compared with KpConv, the proposed FCPNet improved OA, mIOU, F1-score, and Kappa in the NPM3D dataset by 0.36%, 12.11%, 0.77%, and 0.0085, respectively. Especially, the proposed FCPNet is able to classify the objects with fewer point cloud data, such as pedestrians and cars, whose OA can reach 88.04% and 96.42%, respectively. These experimental results demonstrated the proposed FCPNet has rescued much lost information that happened in the traditional PointNet++. In addition, the adaptability to point cloud density variations for the proposed method is verified as well. The results demonstrated that when the total density of point cloud data decreases from 731.3 to 52.2 ${\text{pcs/}}{{\text{m}}^{2}}$, the OA of classification with the proposed FCPNet method only decreases by 3.07%. This means that the proposed FCPNet method is capable of being adaptive to the point cloud density changes.

The inner wall corrosion of pipelines is concealed and hard to monitor in time. In order to visualize the shape of the internally corroded pipeline, as well as the defect volume and its spatial distribution, a three-dimensional(3D) model reconstruction method based on the ultrasonic phased array technology was proposed for the internally corroded pipeline. A self-built acquisition platform was used to control the one-dimensional linear array probes for the phased array, and the reflected echo signal was used to obtain the space coordinates between the inner and outer diameters of the corroded pipeline. Then, the point cloud data set between the inner and outer diameters was constructed by the interpolation method, and the point cloud data of each fracture zone were obtained by the hierarchical scanning method. Besides,data processing, reverse model reconstruction and forward modeling were performed for the 3D model reconstruction of the collected point cloud data. Finally, the reconstructed 3D model was compared with the point cloud data obtained by the Creaform 3D laser scanner. The results show that it is feasible to obtain the point cloud data using one-dimensional linear array probes based on the ultrasonic phased array technology, and the established 3D model and defects have small errors and high precision, which has important reference value for the safety evaluation of oil and gas pipelines.

ABSTRACTRoof vertex information is vital for 3D roof structures. Reconstructing 3D roof structures from point cloud data using traditional methods remains a challenge because their extracted roof vertices are affected by uncertainty and additional errors from roof plane segmentation and supplementary sub-steps for extracting primitives. In this study, instead of segmenting roof planes and then extracting primitives based on them, a flexible rule-based method is proposed to directly detect the vertices of building roofs from point cloud data without the requirement of training data. The point cloud data is first voxelized with a dominant direction-based rotation. Based on the different features of the interior roof points and vertices, rules for voxel filtering and structure line determination are defined to extract the roof vertices. The experimental results on a custom dataset in Trondheim, Norway demonstrate that the proposed method can effectively and accurately extract roof vertices from point cloud data. The comparative experimental results with an unfine-tuned deep learning-based method on custom and benchmark datasets with different point densities further show that the proposed method has good generalization and can adapt to changes of datasets.

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.

The rapid growth in technologies for 3D sensors has made point cloud data increasingly available in different applications such as autonomous driving, robotics, and virtual and augmented reality. This raises a growing need for deep learning methods to process the data. Point clouds are difficult to be used directly as inputs in several deep learning techniques. The difficulty is raised by the unstructured and unordered nature of the point cloud data. So, machine learning models built for images or videos cannot be used directly on point cloud data. Although the research in the field of point clouds has gained high attention and different methods have been developed over the decade, very few research works directly with point cloud data, and most of them convert the point cloud data into 2D images or voxels by performing some pre-processing that causes information loss. Methods that directly work on point clouds are in the early stage and this affects the performance and accuracy of the models. Advanced techniques in classical convolutional neural networks, such as the attention mechanism, need to be transferred to the methods directly working with point clouds. In this research, an attention mechanism is proposed to be added to deep convolutional neural networks that process point clouds directly. The attention module was proposed based on specific pooling operations which are designed to be applied directly to point clouds to extract vital information from the point clouds. Segmentation of the ShapeNet dataset was performed to evaluate the method. The mean intersection over union (mIoU) score of the proposed framework was increased after applying the attention method compared to a base state-of-the-art framework that does not have the attention mechanism.

In Japan, bridges are inspected via close visual examinations every five years. However, these inspections are labor intensive, and a shortage of engineers and budget constraints will restrict such inspections in the future. In recent years, efforts have been made to reduce the labor required for inspections by automating various aspects of the inspection process. In this study, we proposed and evaluated a method of applying super-resolution technology to obtain precise point cloud information to create distance information images to enable the use of tactile information (e.g., human touch) on the surface to be inspected. We measured the distance to the specimen using LiDAR, generated distance information images, performed super-resolution on the pseudo-created low-resolution images, and evaluated them in comparison with the existing magnification method. The evaluation results suggest that the adaptation of the super-resolution technique is effective in increasing the resolution of the boundary of the distance change. [ABSTRACT FROM AUTHOR]

As interest in smart construction technology increases, various smart construction technologies are being used for sustainable construction management. Among these technologies, 3D laser scanning technology stands out for phenomena analysis and monitoring, with various applications being studied for construction management. This study aimed to identify structural members from point cloud data (PCD) obtained through 3D laser scanning and utilize them for the measurement of work progress in construction projects. The method for identifying members is to obtain location coordinate data from the BIM (Building Information Modeling) model of the project and identify the structural member in the PCD by comparing them with the member's location coordinates from the PCD obtained with a 3D laser scanner. In this study, members such as columns, beams, girders, walls, and slabs among the structural members constructed at construction sites were identified through this process. For identified structural members completed at the actual construction site, the unit price and quantity were taken from the construction project's bill of quantity (BOQ) database, and then the Earned Value (EV) was calculated. The results of the study suggest that the progress measurement process through BIM and 3D laser scanning, which was previously performed manually, can contribute to faster and more accurate work progress measurement. Ultimately, it is expected that efficient process management will be possible, contributing to the realization of sustainable construction management. [ABSTRACT FROM AUTHOR]

With the increasing scale of deep-sea oil exploration and drilling platforms, the assessment, maintenance, and optimization of marine structures have become crucial. Traditional detection and manual measurement methods are inadequate for meeting these demands, but three-dimensional laser scanning technology offers a promising solution. However, the complexity of the marine environment, including waves and wind, often leads to problematic point cloud data characterized by noise points and redundancy. To address this challenge, this paper proposes a method that combines K-Nearest-Neighborhood filtering with a hyperbolic function-based weighted hybrid filtering. The experimental results demonstrate the exceptional performance of the algorithm in processing point cloud data from offshore oil and gas platforms. The method improves noise point filtering efficiency by approximately 11% and decreases the total error by 0.6 percentage points compared to existing technologies. Not only does this method accurately process anomalies in high-density areas—it also removes noise while preserving important details. Furthermore, the research method presented in this paper is particularly suited for processing large point cloud data in complex marine environments. It enhances data accuracy and optimizes the three-dimensional reconstruction of offshore oil and gas platforms, providing reliable dimensional information for land-based prefabrication of these platforms. [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 remote sensing transforms forest and urban tree management, automating tree species classification is now a major challenge to harness these advances for forestry and urban management. This study investigated the use of structural bark features from terrestrial laser scanner point cloud data for tree species identification. It presents a novel mathematical approach for describing bark characteristics, which have traditionally been used by experts for the visual identification of tree species. These features were used to train four machine learning algorithms (decision trees, random forests, XGBoost, and support vector machines). These methods achieved high classification accuracies between 83% (decision tree) and 96% (XGBoost) with a data set of 85 trees of four species collected near Krakow, Poland. The results suggest that bark features from point cloud data could significantly aid species identification, potentially reducing the amount of training data required by leveraging centuries of botanical knowledge. This computationally efficient approach might allow for real-time species classification. [ABSTRACT FROM AUTHOR]