Robust Feature-Preserving Denoising of 3D Point Clouds

Haque, Sk. Mohammadul; Govindu, Venu Madhav

doi:10.1109/3dv.2016.17

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Since there was no tunnel-specific method for arch detection that could be used for the control experiment, some feasible methods introduced in Section 3.4.1 were selected, including the profile radius method [37], NARF [40], boundary detection [10], and region-growing. Since the arch point cloud is a feather-edged layer of the point cloud, a Robust Feature-Preserving Denoising (RFPD) method [48] was used to denoise the wire mesh points in scriptProck and preserve the sharp and fine-scale 3D features of arches for NARF [40].…”

Section: Methodsmentioning

confidence: 99%

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Zhang

Qiu

Song

et al. 2019

Sensors

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Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.

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Section: Methodsmentioning

confidence: 99%

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Zhang

Qiu

Song

et al. 2019

Sensors

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“…Point clouds do not directly provide information about the surface topology [20,116], implying that it will be more challenging to accurately estimate an underlying surface or curve representation and to estimate traits related to the surface area, especially in the presence of noise, outliers or other imperfections. This will be even more difficult when dealing with the complex architecture of plants.…”

Section: Point Cloudmentioning

confidence: 99%

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Harandi

Vandenberghe²,

Vankerschaver

et al. 2023

Plant Methods

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Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

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“…However, the method is computationally expensive and fails in detecting outliers in the presence of additive Gaussian noise. 23 Huang et al 24 present an interesting resample-wPCA-based method in which an edge-aware resampling algorithm is used to generate denoised, outlier free and evenly distributed points. Then a sophisticated approach is employed to get reliable orientation for the normals computed by weighted PCA.…”

Section: Related Workmentioning

confidence: 99%

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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Robust Feature-Preserving Denoising of 3D Point Clouds

Cited by 6 publications

References 16 publications

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Robust point cloud normal estimation via neighborhood reconstruction

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