eSphere: extracting spheres from unorganized point clouds

Tran, Trung-Thien; Cao, Van-Toan; Laurendeau, Denis

doi:10.1007/s00371-015-1157-0

Cited by 21 publications

(10 citation statements)

References 35 publications

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“…Spherical objects are crucial primitives found in 3D spatial data. Especially sphere targets are used extensively for camera and laser scanner calibration or data registration in which robust sphere detection and estimation are necessary to achieve good results [13][14][15]. Plenty of approaches for sphere segmentation or extraction from point cloud have been proposed, such as the clustering-based method [16,17], sampling-based method [14,18], and Hough transform-based method [19,20], etc.…”

Section: Algorithms For Sphere Detectionmentioning

confidence: 99%

“…For the detection of hollow spheres during the casting process, however, some fresh concrete may unintentionally drip on the spherical surface and result in wrong calculations of normal vectors and curvatures, probably leading to unsatisfactory clustering results. Besides, some advanced algorithms to handle the multiple sphere detection in a complicated environment may lead to more complexity and computational load [15], which cannot meet the time requirement in this monitoring task. Meanwhile, complex parameter tuning also makes these algorithms difficult to be applied to practical problems.…”

Section: Algorithms For Sphere Detectionmentioning

confidence: 99%

“…At each iteration, the solution of the increment of the parametersx and the improvements V are as follows, where Q L is the cofactor matrix of the observations. Then, the current estimated parametersX are obtained from Equation (15) and used to update the approximate parameters X o while V o is updated by Equation ( 14) directly after each iteration until ∆x is less than the selected accuracy limit (e.g., 10 −6 m).…”

Section: Sphere Fitting and Parameter Estimationmentioning

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: Algorithms For Sphere Detectionmentioning

confidence: 99%

Section: Sphere Fitting and Parameter Estimationmentioning

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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“…In [24,25] the authors presents variants of probabilistic and randomized sampling to determine sphere in 3D points over clouds.…”

Section: Heuristics For Circles Detection In 2d Imagesmentioning

confidence: 99%

Sphere Fitting with Applications to Machine Tracking

Epstein

Feldman

2020

Algorithms

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We suggest a provable and practical approximation algorithm for fitting a set P of n points in R d to a sphere. Here, a sphere is represented by its center x ∈ R d and radius r > 0 . The goal is to minimize the sum ∑ p ∈ P ∣ p − x − r ∣ of distances to the points up to a multiplicative factor of 1 ± ε , for a given constant ε > 0 , over every such r and x. Our main technical result is a data summarization of the input set, called coreset, that approximates the above sum of distances on the original (big) set P for every sphere. Then, an accurate sphere can be extracted quickly via an inefficient exhaustive search from the small coreset. Most articles focus mainly on sphere identification (e.g., circles in 2 D image) rather than finding the exact match (in the sense of extent measures), and do not provide approximation guarantees. We implement our algorithm and provide extensive experimental results on both synthetic and real-world data. We then combine our algorithm in a mechanical pressure control system whose main bottleneck is tracking a falling ball. Full open source is also provided.

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“…shown. First, planar, cylindrical and spherical primitives are extracted from the scans using the method reported in (Tran et al, 2015a;Tran et al, 2016;Tran et al, 2015b). These primitives are then fed to the alignment approach presented in Sec.…”

Section: Application Of the Proposed Approach On Real 3d Scansmentioning

confidence: 99%

3D Geometric Primitive Alignment Revisited

Tran

Cao

Laurendeau

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Rigid registration is an important step in 3D scanning and modeling of manufactured objects that are generally composed of a combination of basic geometric primitives such as planes, spheres, cylinders, etc. In this paper, an efficient and robust method is proposed to align two basic geometric primitives. The transformation between two primitives is found by minimizing the parameter error between primitive correspondences. The approach applies an interior-point method and a new objective function to achieve good results. Compared to previous primitive-based alignment approach proposed by Rabbani et al. (Rabbani et al., 2007), the presented approach achieves better results in terms of convergence and accuracy. Finally, the proposed method is used in various applications such as data completion and primitive-based registration for quality control and inspection.

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eSphere: extracting spheres from unorganized point clouds

Cited by 21 publications

References 35 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

Sphere Fitting with Applications to Machine Tracking

3D Geometric Primitive Alignment Revisited

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