Normal estimation via shifted neighborhood for point cloud

Cao, Junjie; Chen, He; Zhang, Jie; Li, Yujiao; Liu, Xiuping; Zhang, Changqing

doi:10.1016/j.cam.2017.04.027

Cited by 24 publications

(16 citation statements)

References 8 publications

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“…19 But it can be seen from the experimental comparison of the article, its computational efficiency is still lower than other classical algorithms. Cao et al 33 presented a fast and quality normal estimator based on neighborhood shift. Instead of using the neighborhood centering at the current point, a set of neighborhoods containing the current point are evaluated and the one with the most consistent normals is selected as the neighborhood of the current point.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust point cloud normal estimation via neighborhood reconstruction

Wang

Guo

et al. 2019

Advances in Mechanical Engineering

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This article presents a novel method for estimating normals on unorganized point clouds that preserves sharp features. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a neighborhood reconstruction-based normal estimation method is developed to find a proper neighborhood for points around sharp features. A robust statistics-based method is proposed to identify points that near sharp features and classify the points into two categories: edge points and non-edge points. Two specific neighborhood reconstruction strategies are designed for these two types of points to generate a neighborhood clear of sharp features. The normal of the current point can thus be reliably estimated by principal component analysis using the generated isotropic neighborhood. Numerous case studies have been carried out to compare the reliability and robustness of the proposed method against various existing methods. The experiment results show that the proposed approach performs better than the state-of-the-art methods most of the time and offers an ideal compromise between precision, speed, and robustness.

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Section: Related Workmentioning

confidence: 99%

“…In previous studies, 19,20,33 s i is considered as a curvature and used to distinguish edge points, who have a more complicated neighborhood, from non-edge points. The coefficient of a point near sharp features is considered to be larger than that of a point in smooth regions.…”

Section: Edge Point Recognitionmentioning

confidence: 99%

Robust point cloud normal estimation via neighborhood reconstruction

Wang

Guo

et al. 2019

Advances in Mechanical Engineering

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show abstract

“…In [33]- [35], σ i is considered as curvature and used to distinguish edge points, who have a more complicated neighborhood, from non-edge points. The coefficient of a point near sharp features is considered to be larger than that of a point in smooth regions.…”

Section: A Edge Points Identificationmentioning

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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“…Moreover, for the purpose of denoising, most of the methods are using the neighborhood centering at the point to filter the normals, which is prone to produce inaccurate normals. To conquer this problem, Cao et al [CCZ*18] presented a shifted neighborhood scheme to estimate normals. However, this method is not robust to large‐scale noise.…”

Section: Related Workmentioning

confidence: 99%

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Sun

Chen

Qin

et al. 2019

Computer Graphics Forum

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Semantic surface decomposition (SSD) facilitates various geometry processing and product re‐design tasks. Filter‐based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under‐fitting or over‐fitting. In this paper, we propose a reliable rolling‐guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC‐based sub‐neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling‐guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state‐of‐the‐art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation.

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Normal estimation via shifted neighborhood for point cloud

Cited by 24 publications

References 8 publications

Robust point cloud normal estimation via neighborhood reconstruction

Robust point cloud normal estimation via neighborhood reconstruction

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

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

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