•Fragmentation calculation method for open-pit copper blast muck-piles is proposed.•The new method is based on three-dimensional (3D) laser point cloud data (3D LPCD).•The concavity and convexity ...features are considered in the new method.•VCCS algorithm is improved to carry out the super-voxel segmentation.•RANSAC algorithm is used to refine segmented fragments during the LCCP clustering.
The current calculation methods for the blast fragmentation of muck piles (BFMP) do not consider three-dimensional (3D) feature information, such as concavity and convexity. Thus, their calculation results are inaccurate. In this work, 3D laser scanning technology with high precision, high efficiency, and real-time digitization is used to calculate fragmentation of blast muck piles in open-pit copper mines. The voxel cloud connectivity segmentation (VCCS) algorithm and locally convex connected patches (LCCP) algorithm are used to calculate BFMP from 3D laser point cloud data (3D LPCD). Meanwhile, an improved VCCS algorithm based on discrete features is proposed to solve the segmentation problem of 3D LPCD of small blast muck piles, which cannot be effectively segmented and thus greatly influence the 3D LPCD of large blast muck piles. An improved LCCP algorithm based on plane fitting solves the incorrect segmentation of the surface of large fragments into small pieces of fragments. Dexing Copper Mine, the largest open-pit copper mine in China, is taken as the research object. Results show that the accuracy of the calculation results is about 80% when the fragment sizes are 0.1–0.5 m. Moreover, almost all BFMP can be correctly calculated when the fragment size exceeds 0.5 m.
Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital ...construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.
3D spatial measurement for model reconstruction: A review Flores-Fuentes, Wendy; Trujillo-Hernández, Gabriel; Alba-Corpus, Iván Y. ...
Measurement : journal of the International Measurement Confederation,
02/2023, Volume:
207
Journal Article
Peer reviewed
The measurement of 3D spatial coordinates for model reconstruction through artificial machine vision systems based on optical sensors and the corresponding signal processing associated with ...algorithms is a powerful module for cyber systems. It provides an efficient, functional, and intelligent vision and data information of the objects and scenes under observation for decisions, as well as for remote environment interactivity and autonomous robot systems actuation. Over the past 20 years, the artificial machine vision has benefited from emerging technology and a promising huge potential is peeking out, but also technical difficulties achieving customized and true commercial applications. This paper reviews the research progress, trends, and future research directions; the state-of-the-art of topics related to the 3D spatial measurement for model reconstruction. It classifies the technology by its fundamental principles and applications, to construct an outlook about its advantages, disadvantages, and challenges.
•Topics related to the 3D spatial measurement for model reconstruction.•Review of the research progress and the state of the art of topics.•State of the art and the fundamental principles of topics detailed explanation.•Description of technologies and applications are presented.•Classification of technology based on optical sensors and its signal processing.•Technology outlook construction advantages, disadvantages, advances, and challenges.•This literature point out the emergence and boost the machine vision for innovation.•Machine vision based cyber-systems contribution to industry 4.0.•Literature for promotion and encourage of research in related fields.
Terrestrial Laser Scanners (TLS) have gained momentum in identifying cracks and deformations. Although TLS efficiency has been recognized over time, the scan data quality is contingent upon various ...parameters (e.g., material type and scan settings), which impacts the use of TLS. To address this barrier, this study investigates the measurement accuracy in the context of crack analysis in foam board and common building materials (concrete, wood, and masonry) by conducting a set of controlled experiments with different scan settings (e.g., scan distance and angle of incidence). The findings of this study provide practical contributions to using TLS for crack identification, specifically addressing the effect of different parameters on point cloud data quality and proposing boundary ranges for different scan settings regarding different building materials and crack widths to facilitate TLS planning.
•Effect of various TLS parameters on crack width measurement accuracy is studied.•Three common building materials with varying crack widths are tested.•The variations in the error rates are plotted for crack width measurement.•Boundary ranges for different scan settings were proposed.
The main factor affecting beef quality, consumer satisfaction, and purchase decisions is beef tenderness. In this study, a rapid nondestructive testing method for beef tenderness based on airflow ...pressure combined with structural light 3D vision technology was proposed. The structural light 3D camera was used to scan the 3D point cloud deformation information of the beef surface after the airflow acted on it for 1.8 s. Six deformation characteristics and three point cloud characteristics of the beef surface depression region were obtained by using denoising, point cloud rotation, point cloud segmentation, point cloud descending sampling, alphaShape, and other algorithms. A total of nine characteristics were mainly concentrated in the first five principal components (PCs). Therefore, the first five PCs were put into three different models. The results showed that the Extreme Learning Machine (ELM) model had a comparatively higher prediction effect for the prediction of beef shear force, with a root mean square error of prediction (RMSEP) of 11.1389 and a correlation coefficient (R) of 0.8356. In addition, the correct classification accuracy of the ELM model for tender beef achieved 92.96%. The overall classification accuracy reached 93.33%. Consequently, the proposed methods and technology can be applied for beef tenderness detection.
•A beef tenderness detection device was developed independently.•A series of point cloud image processing algorithms were used to segment the space deformation region.•Six phenotypic characteristics and three point cloud characteristics of beef spatial deformation were obtained and analyzed.•Three different prediction and classification models were established and compared.
This article investigates the problem of acquiring 3D object maps of indoor household environments, in particular kitchens. The objects modeled in these maps include cupboards, tables, drawers and ...shelves, which are of particular importance for a household robotic assistant. Our mapping approach is based on PCD (point cloud data) representations. Sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3D geometric shapes. We detail the steps of our mapping approach and explain the key techniques that make it work. The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions.
This paper presents the fundamental mathematics to determine the minimum crack width detectable with a terrestrial laser scanner in unit-based masonry. Orthogonal offset, interval scan angle, crack ...orientation, and crack depth are the main parameters. The theoretical work is benchmarked against laboratory tests using 4 samples with predesigned crack widths of 1–7mm scanned at orthogonal distances of 5.0–12.5m and at angles of 0°–30°. Results showed that absolute errors of crack width were mostly less than 1.37mm when the orthogonal distance varied 5.0–7.5m but significantly increased for greater distances. The orthogonal distance had a disproportionately negative effect compared to the scan angle.
Current approaches for optimizing the placement of roadside LiDAR (RSL) at constructed highways work on handcrafted scenes which fail to precisely map real-world situations. This study proposes a ...computer-aided framework to address the issue. First, high-accuracy point cloud data are introduced to model the as-built highway infrastructures, based on which an unsupervised clustering approach is applied to segment the target monitoring area (TMA). Then, candidate RSL locations are generated in a semi-automated manner combining manual delineation and spline resampling. Next, new deterministic and a U-net-based LiDAR models are separately developed to virtually estimate candidate RSL's joint coverage. Finally, based on the proposed sensor models, a detection matrix is created to facilitate the application of binary integer programming that minimizes the number of RSL while ensuring complete coverage of TMA. The tests on point cloud data of the three different sites demonstrate the effectiveness of the proposed workflow.
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•Introduce point cloud data as road background for optimization of sensors' placement•Propose physics-based and deep neural network-based virtual sensor models•Create a detection matrix to simplify the optimization problem•Test the feasibility of a neural network as a sensor model
Abstract
With the rapid development of computer technology and measurement technology, three-dimensional point cloud data, as an important form of data in computer graphics, is used by light ...reactions in reverse engineering, surveying, robotics, virtual reality, stereo 3D imaging, Indoor scene reconstruction and many other fields. This paper aims to study the key technology of 3D point cloud data multi-view image texture mapping seam fusion, and propose a joint coding and compression scheme of multi-view image texture to replace the previous independent coding scheme of applying MVC standard compression to multi-view image texture. Experimental studies have shown that multi-view texture depth joint coding has different degrees of performance improvement compared with the other two current 3D MVD data coding schemes. Especially for Ballet and Dancer sequences with better depth video quality, the performance of JMVDC is very obvious. Compared with the KS_ IBP structure, the gain can reach as high as 1.34dB at the same bit rate.
The accurate classification of plant organs is a key step in monitoring the growing status and physiology of plants. A classification method was proposed to classify the leaves and stems ...automatically based on the point cloud data of the potted plants. Leaf samples and stem samples were selected automatically by using the three-dimensional (3D) convex hull algorithm and the two-dimensional (2D) projection grid density respectively, and were used to construct the leaf and stem training sets. Then, the point cloud data were classified into leaf points and stem points by using the support vector machine (SVM) algorithm. The point cloud data of three potted plants were used in the experiment. The proposed method was compared with the standard classification, the random selection method and the manual selection method. Among these methods, the proposed method is automated and time-saving. The results show that the proposed method had a good overall performance on accuracy and running time. The proposed method is efficient and effective on the leaf and stem classification of the plant point cloud data.
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•An automated leaf-stem classification method was proposed based on point cloud data.•The 3D convex hull algorithm was used to select leaf sample points automatically.•The projected point density was proposed to select stem sample points automatically.
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