Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

Zhang, Kun; Qiao, Shan; Wang, Xiaohong; Yang, Yongtao; Zhang, Yongqiang

doi:10.3390/app9102130

Cited by 32 publications

(29 citation statements)

References 35 publications

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“…The nonfeature points are simplified using an octree structure to avoid creating regions with holes. Zhang et al [ 19 ] presented a feature-preserved point cloud simplification (FPPS) method. For the simplification, an entropy measure is defined, which quantifies the geometric features hidden in the point cloud.…”

Section: Related Workmentioning

confidence: 99%

A Saliency-Based Sparse Representation Method for Point Cloud Simplification

Leal

Sánchez-Torres

Bedoya

et al. 2021

Sensors

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High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.

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Section: Related Workmentioning

confidence: 99%

A Saliency-Based Sparse Representation Method for Point Cloud Simplification

Leal

Sánchez-Torres

Bedoya

et al. 2021

Sensors

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“…There are different strategies for points selection that can be categorized as global methods (e.g., uniform and random sampling, spatial sampling), local methods (e.g., using geometric information or density information), and feature-based methods. Feature-based methods such as Fast Point Feature Histogram (FPFH) introduced by Rusu et al ( 2009 ), or Feature-Preserved Point cloud Simplification (FPPS) presented by Zhang et al ( 2019 ) use features which describe the local geometry around a point. It reduces then the number of points by grouping them to describe the neighborhood.…”

Section: Related Workmentioning

confidence: 99%

Geometry Preserving Sampling Method Based on Spectral Decomposition for Large-Scale Environments

2020

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In the context of 3D mapping, larger and larger point clouds are acquired with lidar sensors. Although pleasing to the eye, dense maps are not necessarily tailored for practical applications. For instance, in a surface inspection scenario, keeping geometric information such as the edges of objects is essential to detect cracks, whereas very dense areas of very little information such as the ground could hinder the main goal of the application. Several strategies exist to address this problem by reducing the number of points. However, they tend to underperform with non-uniform density, large sensor noise, spurious measurements, and large-scale point clouds, which is the case in mobile robotics. This paper presents a novel sampling algorithm based on spectral decomposition analysis to derive local density measures for each geometric primitive. The proposed method, called Spectral Decomposition Filter (SpDF), identifies and preserves geometric information along the topology of point clouds and is able to scale to large environments with a non-uniform density. Finally, qualitative and quantitative experiments verify the feasibility of our method and present a large-scale evaluation of SpDF with other seven point cloud sampling algorithms, in the context of the 3D registration problem using the Iterative Closest Point (ICP) algorithm on real-world datasets. Results show that a compression ratio up to 97 % can be achieved when accepting a registration error within the range accuracy of the sensor, here for large scale environments of less than 2 cm.

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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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Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

Cited by 32 publications

References 35 publications

A Saliency-Based Sparse Representation Method for Point Cloud Simplification

A Saliency-Based Sparse Representation Method for Point Cloud Simplification

Geometry Preserving Sampling Method Based on Spectral Decomposition for Large-Scale Environments

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

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