Simplified representation for 3D point cloud data

Wang, Lihui; Chen, Jing; Yuan, Baozong

doi:10.1109/icosp.2010.5656972

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…These voxel grids are obtained by using voxelization, which is the process of converting a geometric representation of a point cloud into a set of voxels that accurately represents the investigated point cloud within the discrete voxel space. Voxel grid representations have been used in many applications for both 2D surfaces and 3D models, including medical imaging (Wang et al, 2010) and terrain and surface modeling (Hinks, 2011). For this research, methods that are suitable for processing point data acquired by ALS (Aerial Laser Scanner) and TLS (Terrestrial Laser Scanner) were investigated.…”

Section: Voxelizationmentioning

confidence: 99%

Damage Detection on Structures Using Texture Mapped Laser Point Clouds

Guldur

Hajjar

2014

Structures Congress 2014

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Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense 3D point clouds. However, extracting meaningful information from point clouds automatically is challenging, and the current state-of-the-art requires a certain amount of user-interaction. Previously in our research, widely accepted point cloud processing steps were used to divide laser scanner data into meaningful object clusters, on which several damage detection methods were applied. Our current focus is on combining useful information extracted from laser scanner data with color information since novel laser scanners are capable of recording color data mapped on 3D point clouds. The color data provides additional information in the 4 th dimension, which makes it possible to detect damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect by using only laser scanner data, and also helps to track volumetric changes on structures such as spalling. Even though crack and corrosion detection from images with varying resolution are extensively researched topics, damage detection by using laser scanners with and without images is a new research area that holds many opportunities for enhancing current practice in structural health monitoring. The aim of our research is to benefit from the best features of both laser scans and images, and combine them to create an automatic and effective damage detection method, which will complement the need for skilled labor during visual inspections of damaged structures and allow documenting related information automatically. This work is of interest to researchers who are focused on structural health monitoring and interested in the use of new laser scanners and damage detection algorithms for structures, as well as practitioners, who will benefit exploring new methods for improving their understanding of laser scanning technology and its capabilities for robust damage detection and documentation.

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Section: Voxelizationmentioning

confidence: 99%

Damage Detection on Structures Using Texture Mapped Laser Point Clouds

Guldur

Hajjar

2014

Structures Congress 2014

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“…Point cloud density information cannot be used as the basis for point cloud simplification, but it can provide a reasonable reference for the importance evaluation of points. Consequently, considering the density feature index can help us to better retain detailed information on point clouds [ 14 , 28 ].…”

Section: Related Researchmentioning

confidence: 99%

“…Additionally, some researchers have utilized more than one feature. Wang et al [ 14 ] defined a comprehensive feature index including the average distance, normal, and curvature to distinguish feature points. After that, the non-feature points are simplified by uniform spherical sampling.…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors

Shi

Meng

2022

Sensors

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Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.

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“…Unlike distance-based rules, statistical rules were used to refine the clusters base on expectation maximization algorithm, where the objective function is log-likelihood measuring how well the probabilistic subset fits the point cloud dataset [23], [24]. Another widely accepted rule is the local feature, which was estimated and clustered based on curvatures [25], [26], vertices and boundaries [27], [28], angle parameters [29], eigenvalues [30], natural quadric shapes [31], dual quadric metric [32], graph [33], and thresholdindependent Bayesian sampling consensus [34]. A recent work in [33] realized a uniform resampling while preserving the local features through normalized Laplacian and a k-nearest-neighbor graph.…”

Section: Fig1 Application Framework Of Automatic Assembly Line Basedmentioning

confidence: 99%

Fast Assembly Tolerance Inspection Method Using Feature-Based Adaptive Scale Reduction in Automatic Assembly Line

Qiu

Zhang

et al. 2020

IEEE Access

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Enormous computation and long data processing time of the online tolerance inspection procedure has been a bottleneck in reducing the production cycle time of automatic assembly line. To reduce time consumption while maintaining accuracy, a fast assembly tolerance inspection method is proposed in this paper using scale-reduced models. First, the general part surface, input by a laser-scanned dense point cloud, is segmented through feature-based clustering. Shape variation estimation are designed to determine reduction ratios between clusters adaptively. Then, assembly simulation method is further advanced to utilize coarse point models with imbalanced density. Finally, numerical experiments are designed to estimate both static and dynamic assembly tolerances as results of tolerance inspection. A case study on a motion component of a rail material transporter shows that the proposed method could reduce 81% of the data processing time compared to traditional practice. The precision loss has minor influence on the inspection results because spatial positioning error is no more than 6% of tolerance interval and 18% of scanning resolution. Moreover, the proposed method has a comprehensive advantage of time and accuracy than two conventional point cloud simplification techniques based on iterative and clustering methods.

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Simplified representation for 3D point cloud data

Cited by 9 publications

References 7 publications

Damage Detection on Structures Using Texture Mapped Laser Point Clouds

Damage Detection on Structures Using Texture Mapped Laser Point Clouds

A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors

Fast Assembly Tolerance Inspection Method Using Feature-Based Adaptive Scale Reduction in Automatic Assembly Line

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