Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization

Wang, Yuan; Wang, Jiajing; Chen, Xiuwan; Chu, Tianguang; Liu, Maolin; Yang, Ting

doi:10.3390/rs10071073

Cited by 11 publications

(7 citation statements)

References 32 publications

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“…In order to avoid the high complexity and operational difficulty as well as guarantee the robustness in a changing scenario, the region growing (RG) and the random sample consensus (RANSAC) algorithms are considered for multiple sphere segmentation or detection from the 3D point cloud in this monitoring task due to their superiorities. RG was first proposed in the field of intensity image segmentation [21] and then was introduced to 3D point cloud segmentation [22][23][24][25][26]. This method has the advantage of unnecessary prior parameters of spheres (like the design of the sphere layout and the coordinate information).…”

Section: Algorithms For Sphere Detectionmentioning

confidence: 99%

“…The 3D RG typically starts from one or more points (seed points) featuring specific characteristics and then gathers the nearest neighbors in the seed area on the basis of certain constraints such as similar surface orientations or curvatures [26]. Constraints of surface normal vectors and curvatures were widely used to find the smoothly connected areas that would be clustered as specific regions [23,25,41,42].…”

Section: Preliminary Segmentation Using 3d Region Growingmentioning

confidence: 99%

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Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

et al. 2021

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Accepting the ecological necessity of a drastic reduction of resource consumption and greenhouse gas emissions in the building industry, the Institute for Lightweight Structures and Conceptual Design (ILEK) at the University of Stuttgart is developing graded concrete components with integrated concrete hollow spheres. These components weigh a fraction of usual conventional components while exhibiting the same performance. Throughout the production process of a component, the positions of the hollow spheres and the level of the fresh concrete have to be monitored with high accuracy and in close to real-time, so that the quality and structural performance of the component can be guaranteed. In this contribution, effective solutions of multiple sphere detection and concrete surface modeling based on the technology of terrestrial laser scanning (TLS) during the casting process are proposed and realized by the Institute of Engineering Geodesy (IIGS). A complete monitoring concept is presented to acquire the point cloud data fast and with high-quality. The data processing method for multiple sphere segmentation based on the efficient combination of region growing and random sample consensus (RANSAC) exhibits great performance on computational efficiency and robustness. The feasibility and reliability of the proposed methods are verified and evaluated by an experiment monitoring the production of an exemplary graded concrete component. Some suggestions to improve the monitoring performance and relevant future work are given as well.

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Section: Algorithms For Sphere Detectionmentioning

confidence: 99%

Section: Preliminary Segmentation Using 3d Region Growingmentioning

confidence: 99%

Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

et al. 2021

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“…Feature recognition of point cloud patches is necessary for multi-sensor data fusion in this paper. Wang et al [38] propose a random sample consensus (RANSAC) algorithm for point cloud data feature recognition. RANSAC method is a statistical-based approach, which can accurately identify the features of point cloud data patches and derive the expression of features even if the data patches contain a large number of outliers.…”

Section: Feature Recognitionmentioning

confidence: 99%

Rapid and Precise Reverse Engineering of Complex Geometry Through Multi-Sensor Data Fusion

Wang

et al. 2019

IEEE Access

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This paper presents an effective multi-sensor approach for improving the accuracy of laser scanner by using a tactile probe to perform rapid and accurate reverse engineering of complex objects. The proposed system is unique in that it includes not only the physical integration of the two digitizers but also their combination at the measurement information level. With the coordinate data acquired using the optical scanner, intelligent data segmentation and feature recognition algorithm are proposed to divide the original point set into geometric elements and free-form surfaces. The tactile probe is guided to re-measure each feature with a small number of sampling points. Then different data fusion algorithms are proposed to compensate the scanned data patches of geometric features and free-form features with the accurate tactile probing data. Finally, the compensated point data can be exploited for accurate reverse engineering of a CAD model. Through the fusion of different sensor's information, both sensors complement each other with their advantages. Experimental results show that for the measurement of geometric surface, the proposed method can improve the accuracy of optical measurement to the accuracy of contact measurement. For freeform surface measurement, the proposed method reduces the optical measurement error from +0.032/-0.019 mm to ±0.016 mm, and the standard deviation from 0.012 mm to 0.004 mm. The measurement efficiency of proposed method is 4.5 times higher than that of contact measurement alone. INDEX TERMS Multi-sensor, reverse engineering, tactile probe, optical scanner, data fusion.

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“…In the problem of the edge-based tracking of a rock mass target, there are mainly two aspects of research questions: where to track and how to get the 3D model for matching (detailed in Section 2.3). Regarding the first aspect, compared with the industrial applications aiming at well trackable geometries, the irregularly fractured morphology can appear heterogeneous in the spatial distribution of edge features, where the scales of extending, winding, and crisscrossing of the edges detected in camera images can vary tremendously in different parts of the rock mass [37,38]. Consequently, the slope region with the most favorable visual features for edge alignment needs to be delineated first.…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

et al. 2019

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In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation.

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Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization

Cited by 11 publications

References 32 publications

Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

Rapid and Precise Reverse Engineering of Complex Geometry Through Multi-Sensor Data Fusion

Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

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