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In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R2) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production. [ABSTRACT FROM AUTHOR]

Phenotypic analysis of mature soybeans is a critical aspect of soybean breeding. However, manually obtaining phenotypic parameters not only is time-consuming and labor intensive but also lacks objectivity. Therefore, there is an urgent need for a rapid, accurate, and efficient method to collect the phenotypic parameters of soybeans. This study develops a novel pipeline for acquiring the phenotypic traits of mature soybeans based on three-dimensional (3D) point clouds. First, soybean point clouds are obtained using a multi-view stereo 3D reconstruction method, followed by preprocessing to construct a dataset. Second, a deep learning-based network, PVSegNet (Point Voxel Segmentation Network), is proposed specifically for segmenting soybean pods and stems. This network enhances feature extraction capabilities through the integration of point cloud and voxel convolution, as well as an orientation-encoding (OE) module. Finally, phenotypic parameters such as stem diameter, pod length, and pod width are extracted and validated against manual measurements. Experimental results demonstrate that the average Intersection over Union (IoU) for semantic segmentation is 92.10%, with a precision of 96.38%, recall of 95.41%, and F1-score of 95.87%. For instance segmentation, the network achieves an average precision (AP@50) of 83.47% and an average recall (AR@50) of 87.07%. These results indicate the feasibility of the network for the instance segmentation of pods and stems. In the extraction of plant parameters, the predicted values of pod width, pod length, and stem diameter obtained through the phenotypic extraction method exhibit coefficients of determination ( R 2 ) of 0.9489, 0.9182, and 0.9209, respectively, with manual measurements. This demonstrates that our method can significantly improve efficiency and accuracy, contributing to the application of automated 3D point cloud analysis technology in soybean breeding. [ABSTRACT FROM AUTHOR]

The existing method of gangue location, primarily relying on 2D coordinates and simplified 3D coordinates, often results in distorted position information, leading to failures in gangue sorting. In this paper, we propose region growing with two-component feature (RG-TCF) algorithm to segment the complete and uncut point cloud of coal and gangue for accurate 3D gangue location. Firstly, the workflow of RG-TCF was developed by the advantage of fast point feature histograms (FPFH) over the angle between two normal vectors used in RG (region growing). Secondly, the extraction, validation and test sets were built based on the production and annotation of point cloud. Thirdly, after eliminating noise and redundant points with the proposed down-sampling based on key point (DS-KP), segmentation thresholds of two-component feature were also worked out by histogram analysis. Finally, the performance of RG-TCF was validated and tested by segmentation and location experiments. It could be concluded that RG-TCF improved the under-segmentation effectively; it increased Dice coefficient and location precision by 10.8% and 9.2% compared with those of the popular segmentation algorithms, respectively. [ABSTRACT FROM AUTHOR]

Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed. [ABSTRACT FROM AUTHOR]

Graph Convolutional Neural Networks (GCNs) have demonstrated significant efficiency and flexibility in processing irregular data. And since point clouds are essentially irregular points discretely distributed in space, GCNs have great potential for application in point cloud segmentation tasks. However, current GCNs struggle to learn global structure and local details efficiently. Furthermore, their excessive use of Max pooling results in the substantial loss of contextual structural information. To address the above problems, we present a novel hierarchical graph structure learning network (SGSLNet), which mainly consists of structure-aware Adaptive Graph Convolution (GAdaptive Conv) and Stratified Contextual Graph Pooling (SCGP). GAdaptive Conv is used to dynamically learn local geometric structures, while SCGP is employed to aggregate features and model global contextual structures. Our method not only learns global structure and local details concurrently but also reduces the loss of contextual structure information. We conduct extensive experiments on various datasets, including ShapeNetPart, S3DIS, and ScanNet v2. The results show that SGSLNet achieves state-of-the-art segmentation performance. [ABSTRACT FROM AUTHOR]

The extraction of geometric features such as holes, arcs, and surfaces of mechanical parts is crucial for quality control. The existing methods for geometrical feature segmentations on 3D point clouds still have limitations, especially for simultaneously extracting multiple types of geometric features from comprehensive workpieces. To this end, this study investigates segmentation methods that take 3D point cloud datasets of mechanical parts as inputs, and employs an improved PointNet++ deep learning model to solve this extraction difficulty. Firstly, the Set Abstraction module in PointNet++ was modified by incorporating Self-Attention mechanisms to increase interactivity and global correlation among data points. Then, the local feature extraction Multilayer Perceptron (MLP) from PointNet-Transformer was integrated to enhance the feature extraction accuracy. Due to the inherent class imbalance issue, the Focal Tversky Loss is employed as the loss function to ensure that geometric features with relatively lower proportions can be fully trained. Finally, the Statistical filtering algorithm is utilized to mitigate noise and attenuate subtle irregularities, such that the smoothness of geometric features can be substantially enhanced. The experimental results demonstrate that the proposed model achieves an accuracy of 86.6% on geometric feature segmentations and a mean Intersection over Union (mIoU) of 0.84. The comparison with the original PointNet++ proves that the proposed method can improve accuracy and mIoU by 3.7% and 0.03 respectively. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy and poor robustness of traditional point cloud segmentation algorithms, a point clouds network++ based on K-nearest neighbor algorithm and feature fusion (KF-PointNet++) three-dimensional point cloud segmentation algorithm was proposed. Firstly, the K-nearest neighbors (KNN) algorithm was used to group the point cloud. Secondly, the local features in point clouds network (PointNet) were spliced with the global features of the center point to enhance the ability of the algorithm to capture geometric details and global context, so as to improve the segmentation accuracy and robustness of the algorithm and realize the accurate segmentation of rape point cloud organs. The self-made rape point cloud data set was used for experiments. The results showed that the overall accuracy (OA) of the KF-PointNet ++ algorithm in rape point cloud segmentation could reach 97.1%, and the mean intersection over union (mloU) was 86.4%. The KF-PointNet++ algorithm was superior to the classical PointNet, PointNet++ and kernal point convolution (KPConv) in segmentation performance. It shows high accuracy and strong robustness in point cloud segmentation tasks, which can provide a reliable basis for subsequent rapeseed phenotype research. [ABSTRACT FROM AUTHOR]

Automation in the construction has seen progress in using modern techniques, which has opened new perspectives for the verification of construction structures using point clouds. This paper discusses wall structure geometry verification, using point cloud data with geometry information extracted from building information modeling models as reference data. The research is focusing on automating the verification of wall structures using a software solution developed in Python. It involves processing and extracting geometric data from models in industry foundation classes' format, comparing the data and visualization of deviation. Results, conclusion, and future workplans are given for achieving better understanding. [ABSTRACT FROM AUTHOR]

With the advancement of industrial automation, the frequency of human–robot interaction (HRI) has significantly increased, necessitating a paramount focus on ensuring human safety throughout this process. This paper proposes a simulation‐assisted neural network for point cloud segmentation in HRI, specifically distinguishing humans from various surrounding objects. During HRI, readily accessible prior information, such as the positions of background objects and the robot's posture, can generate a simulated point cloud and assist in point cloud segmentation. The simulation‐assisted neural network utilizes simulated and actual point clouds as dual inputs. A simulation‐assisted edge convolution module in the network facilitates the combination of features from the actual and simulated point clouds, updating the features of the actual point cloud to incorporate simulation information. Experiments of point cloud segmentation in industrial environments verify the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Intelligent analysis of 3D point clouds has become a frontier in emerging fields such as autonomous driving, digital twins, and the metaverse. Precise segmentation of 3D point clouds is particularly important within these domains; however, it faces several challenges: (1) point cloud data inherently lacks structured topological information; (2) point cloud shapes are complex and highly variable, making it difficult to utilize semantic priors; and (3) the sampling process of point clouds may result in sparse and uneven data. To address these issues, this paper proposes a novel Point Cloud Segmentation Network based on multi-scale feature fusion and Transformer architecture (MFFTNet). MFFTNet enhances the performance of existing segmentation methods by globally modeling the overall point cloud shape and embedding local point cloud details. Specifically, MFFTNet divides the segmentation task into encoding and decoding stages. The encoder is designed as a hierarchical pyramid structure that extracts relatively sparse local center points and fuses local features during progressive downsampling. It also utilizes a Transformer for global feature modeling to establish multi-scale topological and semantic information of the point cloud. Subsequently, multi-scale feature fusion further enhances the network’s perception of local features and global structure. The decoder progressively upsamples to restore the original point cloud and injects multi-scale feature information to achieve precise segmentation. Based on the aforementioned encoding-decoding structure and multi-scale feature fusion, MFFTNet outperforms existing methods on the point cloud semantic segmentation datasets ShapeNetPart and S3DIS.

Identifying shapeless granular materials in complex construction scenarios is critical for achieving automation in engineering equipment such as wheel loaders. The challenges of segmenting point clouds for granular materials involve dealing with sparsity, real-time processing requirements, the lack of distinct shape representation, and the issue of different materials sharing similar shapes. This paper proposes MVSRF, a real-time multi-view based point cloud semantic segmentation method incorporating a single-frame re-segmentation component and a multi-frame semantic filter to enhance accuracy and robustness. First, the segmentation system generates a sparse pixel-depth grid map via semantic projection to encapsulate global points and their behaviors, while employing an edge detector to label boundary points around objects. Second, a zero-shot re-segmentation algorithm involving seed extension, novel one-dimensional DBSCAN, Delaunay triangulation, and semantic reassignment corrects mis-segmented points caused by mapping bias. Finally, a lightweight semantic filter is designed to suppress semantic noise during multiple observations. We have built a multi-sensor platform on a wheel loader and collected experimental data to verify the effectiveness of our method. Two optimization components illustrated exceptional performance on the annotated dataset. The MVSRF method possesses strong robustness against external calibration errors, camera pose estimation errors, and inaccurate image segmentation, providing a practical solution for real-time perception of granular materials. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) point clouds have emerged as a predominant data source for the generation of digital elevation models (DEM) in recent years. Traditionally, the generation of DEM using ALS point clouds involves the steps of point cloud classification or ground point filtering to extract ground points and labor-intensive post-processing to correct the misclassified ground points. The current deep learning techniques leverage the ability of geometric recognition for ground point classification. However, the deep learning classifiers are generally trained using 3D point clouds with simple geometric terrains, which decrease the performance of model inferencing. In this study, a point-based deep learning model with boosting ensemble learning and a set of geometric features as the model inputs is proposed. With the ensemble learning strategy, this study integrates specialized ground point classifiers designed for different terrains to boost classification robustness and accuracy. In experiments, ALS point clouds containing various terrains were used to evaluate the feasibility of the proposed method. The results demonstrated that the proposed method can improve the point cloud classification and the quality of generated DEMs. The classification accuracy and F1 score are improved from 80.9% to 92.2%, and 82.2% to 94.2%, respectively, by using the proposed methods. In addition, the DEM generation error, in terms of mean squared error (RMSE), is reduced from 0.318–1.362 m to 0.273–1.032 m by using the proposed ensemble learning. [ABSTRACT FROM AUTHOR]

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

Seepage failure of levees can cause landslides and other hazards. Three-dimensional (3D) laser-scanning technology has become a new method for collecting levee hazard data. In this study, the 3D characteristics of landslide point clouds were investigated through systematic indoor model tests, and a feature recognition algorithm applicable to levee landslides was proposed. The major outcomes of this study are as follows: 1) the formulation of an adaptive random sampling boundary extraction (A-R-B) algorithm, which integrates random sample consensus plane segmentation, adaptive distance threshold calculation, and boundary extraction for levee landslide disaster recognition; 2) through feasibility analyses and accuracy tests of the A-R-B algorithm, this study demonstrated the capacity of the proposed method to accurately recognise the features of levee landslides, with a relative accuracy of 1 cm and an absolute accuracy of 3.5 cm in the extraction process; 3) the testing of the A-R-B algorithm and optimal parameters for the recognised levee landslide features using the point clouds obtained from laboratory models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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.)

This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted γ curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters. [ABSTRACT FROM AUTHOR]

Faults generated by seismic motion and stratigraphic lithology changes are essential research objects for seismic motion and hydrocarbon prospecting. This paper emphatically concentrates on the fault reconstruction from the existing fault probability volume. The core idea is to transform the separation of different fault sticks into a fitting and segmentation problem of point cloud data. First, we utilize the point cloud filtering algorithm to preprocess the probability volume and then complete the coarse segmentation of the fault sticks by the region growth algorithm. For the intersecting faults, we employ an enhanced random sample consensus methodology with the constraints of fault orientation and effective inliers to accomplish the detailed segmentation of different fault sticks. Finally, we take the faults identified by the region growth and the random sample consensus method as a priori to construct a random forest model to predict the fault sticks of additional data. By examining and comparing the proposed method with some other approaches with both synthetic and field data, the experimental results manifest that the novel method achieves better segmentation results than others. Moreover, the proposed method is efficient based on the fact that it can handle billions of voxels within a few minutes. [ABSTRACT FROM AUTHOR]

Maize growth parameters are key pieces of information describing the growth, development and yield of plants, and they are of great significance for high quality and high yield maize breeding. Plants counting, row spacing, and plant spacing are closely related to maize sowing quality and yield. Plant height is closely related to maize photosynthetic rate and biomass. The main problem with measuring phenotypic parameters during the maize seedling stage is that maize plants are small, making it difficult to segment maize plant point clouds from ground point clouds, and there is a lack of automatic methods for measuring growth parameters. A point cloud intensity segmentation and improved Euclidean clustering method for single plant segmentation was proposed based on terrestrial laser scanning (TLS) technical, and an automatic measuring method of growth parameters of maize seedling stage was realized. First, TLS was used to scan maize plants at two planting densities at V1 (first leaf is fully expanded) and V2 (second leaf is fully expanded) stages. Second, the segmentation based on point cloud intensity method and the improved Euclidean clustering single plant segmentation method were used to achieve the segmentation of a single maize plant. Finally, the automatic measurement of maize plants counting, row spacing, plant spacing, and plant height were realized. When plants counting of maize, the percentage error was lower than 1.70%. In the measurement of row spacing, plant spacing and plant height of maize plants, the mean absolute percentage error (MAPE) were lower than 1.50%, 5.50% and 3.10%, respectively. These results demonstrate that the point cloud intensity segmentation and growth parameters measurement method is feasible for maize V1 and V2 stages and different planting densities. The automatic measurement values of the number of maize plants, row spacing, plant spacing and plant height has high measurement accuracy and small error. This study provides a rapid, automatic and accurate measurement method for researchers to measure maize growth parameters.

ObjectiveThe body size parameter of cattle is a key indicator reflecting the physical development of cattle, and is also a key factor in the cattle selection and breeding process. In order to solve the demand of measuring body size of beef cattle in the complex environment of large-scale beef cattle ranch, an image acquisition device and an automatic measurement algorithm of body size were designed.MethodsFirstly, the walking channel of the beef cattle was established, and when the beef cattle entered the restraining device through the channel, the RGB and depth maps of the image on the right side of the beef cattle were acquired using the Inter RealSense D455 camera. Secondly, in order to avoid the influence of the complex environmental background, an improved instance segmentation network based on Mask2former was proposed, adding CBAM module and CA module, respectively, to improve the model's ability to extract key features from different perspectives, extracting the foreground contour from the 2D image of the cattle, partitioning the contour, and comparing it with other segmentation algorithms, and using curvature calculation and other mathematical methods to find the required body size measurement points. Thirdly, in the processing of 3D data, in order to solve the problem that the pixel point to be measured in the 2D RGB image was null when it was projected to the corresponding pixel coordinates in the depth-valued image, resulting in the inability to calculate the 3D coordinates of the point, a series of processing was performed on the point cloud data, and a suitable point cloud filtering and point cloud segmentation algorithm was selected to effectively retain the point cloud data of the region of the cattle's body to be measured, and then the depth map was 16. Then the depth map was filled with nulls in the field to retain the integrity of the point cloud in the cattle body region, so that the required measurement points could be found and the 2D data could be returned. Finally, an extraction algorithm was designed to combine 2D and 3D data to project the extracted 2D pixel points into a 3D point cloud, and the camera parameters were used to calculate the world coordinates of the projected points, thus automatically calculating the body measurements of the beef cattle.Results and DiscussionsFirstly, in the part of instance segmentation, compared with the classical Mask R-CNN and the recent instance segmentation networks PointRend and Queryinst, the improved network could extract higher precision and smoother foreground images of cattles in terms of segmentation accuracy and segmentation effect, no matter it was for the case of occlusion or for the case of multiple cattles. Secondly, in three-dimensional data processing, the method proposed in the study could effectively extract the three-dimensional data of the target area. Thirdly, the measurement error of body size was analysed, among the four body size measurement parameters, the smallest average relative error was the height of the cross section, which was due to the more prominent position of the cross section, and the different standing positions of the cattle have less influence on the position of the cross section, and the largest average relative error was the pipe circumference, which was due to the influence of the greater overlap of the two front legs, and the higher requirements for the standing position. Finally, automatic body measurements were carried out on 137 beef cattle in the ranch, and the automatic measurements of the four body measurements parameters were compared with the manual measurements, and the results showed that the average relative errors of body height, cross section height, body slant length, and tube girth were 4.32%, 3.71%, 5.58% and 6.25%, respectively, which met the needs of the ranch. The shortcomings were that fewer body-size parameters were measured, and the error of measuring circumference-type body-size parameters was relatively large. Later studies could use a multi-view approach to increase the number of body rule parameters to be measured and improve the accuracy of the parameters in the circumference category.ConclusionsThe article designed an automatic measurement method based on two-dimensional and three-dimensional contactless body measurements of beef cattle. Moreover, the innovatively proposed method of measuring tube girth has higher accuracy and better implementation compared with the current research on body measurements in beef cattle. The relative average errors of the four body tape parameters meet the needs of pasture measurements and provide theoretical and practical guidance for the automatic measurement of body tape in beef cattle.

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain's shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status. [ABSTRACT FROM AUTHOR]

Generating as-built Building information models (BIMs) is promising in power substation construction projects because they can reflect the actual conditions of facilities. However, traditional manual-designed BIMs are different from real-world scenarios due to reality gaps. In this paper, we present a new method of reconstructing the layout of power equipment and facilities in substations using LIDAR point clouds. The proposed method extracts electric equipment and facilities via object segmentation and model retrieval. In particular, we investigate PFH, FPFH and SHOT descriptors for the 3D-SIFT keypoints in the 3D shape retrieval of complex electric equipment and facilities. After the best-match model is retrieved from a model library, the layout of typical electric equipment and facilities is reconstructed by aligning the model to the scene point cloud via point cloud registration. Experimental results validate the effectiveness of the proposed method. The proposed method enhances the efficiency of generating 3D models of power substations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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 Smart Agriculture is the property of Smart Agriculture 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.)

Film cooling is an effective technology for improving the thrust-to-weight ratio of an aero-engine. The axial orientation of film cooling holes is one of the key factors determining the film cooling effect. However, the machining quality of cooling holes is still carried out by manual inspection, whose accuracy and consistency cannot be guaranteed. To address this problem, this paper proposes an axial orientation extraction algorithm based on laser point cloud processing. Firstly, the blade surface is scanned by a line laser sensor and a blade point cloud is obtained. Secondly, a single film hole can be segmented from the blade point cloud based on scanning line fitting residual. Thirdly, an improved Gaussian map is to complement the arc belt obtained by Gaussian map to obtain a complete circle along an error-sensitive direction. Finally, the axial orientation is estimated by random sample consensus with an initial value. The accuracy and consistency of the algorithm are verified by comparing the extraction result by the algorithm with those by the Geomagic software. The result shows that the axial accuracy extracted by the algorithm can reach 0.405°. [ABSTRACT FROM AUTHOR]

In recent years, the classification and segmentation research of point cloud mostly adopts the method of extracting the features of point cloud with multi-level architecture, and obtains relatively stable high-level semantic features. However, the extraction of global features and neighborhood features are insufficient, and the feature fusion of context information is lacking. Therefore, a new LAM-EdgeCNN network is proposed in this paper, which adopts the cascade of edge convolution and attention mechanism to extract multi-level feature information from point clouds and obtain high-level feature information. Firstly, in order to enhance the capture of specific channel features and key spatial points, a lightweight LAM attention mechanism is proposed, which uses CAM feature channel attention to acquire the correlation of each channel and locate the capture of key channel features, so that the network pays more attention to specific channel features to reduce the information dispersion and feature redundancy. Secondly, SAM spatial attention mechanism is introduced to obtain the attention weight of the location information of the point space and increase the granularity of the shallow information. Finally, a combination of attention mechanism and edge convolution EdgeConv is used to enhance context awareness, fully extract and fuse the local features and context features of point cloud, and obtain the downstream task-oriented point cloud features. The model is applied to the public data set, and the experiment shows that the model has good effect and robustness in the tasks of point cloud classification, component segmentation and semantic segmentation. [ABSTRACT FROM AUTHOR]

Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations, which has not been well studied. We observe that the straightforward way of applying consistency constraints to weakly-supervised point cloud segmentation has two major limitations: noisy pseudo labels due to the conventional confidence-based selection and insufficient consistency constraints due to discarding unreliable pseudo labels. Therefore, we propose a novel Reliability-Adaptive Consistency Network (RAC-Net) to use both prediction confidence and model uncertainty to measure the reliability of pseudo labels and apply consistency training on all unlabeled points while with different consistency constraints for different points based on the reliability of corresponding pseudo labels. Experimental results on the S3DIS and ScanNet-v2 benchmark datasets show that our model achieves superior performance in weakly-supervised point cloud segmentation. The code will be released publicly at https://github.com/wu-zhonghua/RAC-Net. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology 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.)