Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood

2010 ◽  
Vol 97-101 ◽  
pp. 3631-3636 ◽  
Author(s):  
Xin He Liang ◽  
Jin Liang ◽  
Chen Guo
Keyword(s):  
Numerical Experiment ◽  
Shape Factor ◽  
Point Cloud ◽  
Surface Feature ◽  
Normal Space ◽  
Denoising Method ◽  
Local Shape ◽  
Trilateral Filter ◽  
Sharp Edges ◽  
Two Stages

We present a scatter point cloud denoising method, which can reduce noise effectively, while preserving mesh features such as sharp edges and corners. The method consists of two stages. Firstly, noisy points normal are filtered iteratively; second, location noises of points are reduced. How to select proper denoising neighbors is a key problem for scatter point cloud denoising operation. The local shape factor which related to the surface feature is proposed. By using the factor, we achieved the shape adaptive angle threshold and adaptive optimal denoising neighbor. Normal space and location space is denoising using improved trilateral filter in adaptive angle threshold. A series of numerical experiment proved the new denoising algorithm in this paper achieved more detail feature and smoother surface.

SVM Ensemble-based Noise Detection Method for Image Denoising

2020 ◽  
Vol 13 (8) ◽  
pp. 1153-1165
Author(s):  
Xiaofen Jia ◽  
Chen Wang ◽  
Yongcun Guo ◽  
Baiting Zhao ◽  
Yourui Huang
Keyword(s):  
Mean Value ◽  
Classification Model ◽  
Support Vector ◽  
Noise Detection ◽  
Denoising Method ◽  
Noise Point ◽  
Noise Classification ◽  
Sharp Edges ◽  
Two Stages ◽  
Denoised Image

Background: To preserve sharp edges and image details while removing noise, this paper presents a denoising method based on Support Vector Machine (SVM) ensemble for detecting noise. Methods: The proposed method ISVM can be divided into two stages: noise detection and noise recovery. In the first stage, local binary features and weighted difference features are extracted as input features vector of ISVM, and multiple sub-SVM classifiers are integrated to form the noise classification model of ISVM by iteratively updating the sample weight. The pixels are divided into noise points and signal points. In the noise recovery stage, according to the classification results of the previous stage, only the gray value of the noise point is replaced, and the replacement value is the weighted mean value with the reciprocal of the quadratic square of the distance as the weight. Results: Finally, the replacement value at the noise point and the original pixel value of the signal point are reconstructed to get the denoised image. Conclusion: The experiments demonstrate that ISVM can achieve a noise detection rate of up to 99.68%. ISVM is highly effective in the denoising task, produces a visually pleasing denoised image with clear edge information, and offers remarkable improvement compared to that of the BPDF and DAMF.

scholarly journals 3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

2021 ◽  
Vol 13 (11) ◽  
pp. 2145
Author(s):  
Yawen Liu ◽  
Bingxuan Guo ◽  
Xiongwu Xiao ◽  
Wei Qiu
Keyword(s):  
Real Data ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Denoising Method ◽  
3D Mesh ◽  
Mesh Denoising ◽  
Scene Structure ◽  
Sharp Edges ◽  
Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

Application of Number Density Transport Equation for the Recovery of Consistency in Multi-Field Model

2003 ◽  
Author(s):  
Akio Tomiyama ◽  
Naoki Shimada ◽  
Hiroyuki Asano
Keyword(s):  
Numerical Experiment ◽  
Transport Equation ◽  
Shape Factor ◽  
Field Model ◽  
Fluid Model ◽  
Fluid Models ◽  
Number Density ◽  
Two Phase ◽  
Closure Relations ◽  
Dispersed Flows

It is demonstrated through a thought numerical experiment that a conventional two- or multi-fluid model suffers from an inconsistency problem, by which it would fail in accurately predicting two-phase dispersed flows even with reliable closure relations for interfacial transfer terms. To overcome the inconsistency, a numerical method based on a number density transport equation and a shape factor for a fluid or solid particle is proposed. The (N+2)-field model (NP2 model) proposed in our previous studies [1]–[3] is adopted as the basis of the proposed method. It is confirmed that the method gives better predictions than conventional multi-fluid models and recovers the consistency.

Robust Curvature Estimation on Scattered Point Cloud

2013 ◽  
Vol 303-306 ◽  
pp. 2198-2202
Author(s):  
Fan Zhang ◽  
Bao Sheng Kang ◽  
Jian Dong Zhao
Keyword(s):  
Point Cloud ◽  
Robust Statistics ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Sample Point ◽  
Local Shape ◽  
Curvature Estimation

A robust statistics approach to curvature estimation on scattered point cloud is presented. The basic idea of this method is fitting a surface to the local shape at a sample point in 3D and the curvatures are computed for this fitted surface. Within a Maximum Kernel Density Estimator framework, the best fitted surface for each point is obtained. Therefore the algorithm is robust with respect to noise and outliers. Experiments show that our method has achieved satisfactory results.

scholarly journals A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner

2014 ◽  
Vol XL-5 ◽  
pp. 573-580 ◽  
Author(s):  
K. Thoeni ◽  
A. Giacomini ◽  
R. Murtagh ◽  
E. Kniest
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
Digital Cameras ◽  
Rock Wall ◽  
Geological Features ◽  
Dense Point ◽  
Sharp Edges

This work presents a comparative study between multi-view 3D reconstruction using various digital cameras and a terrestrial laser scanner (TLS). Five different digital cameras were used in order to estimate the limits related to the camera type and to establish the minimum camera requirements to obtain comparable results to the ones of the TLS. The cameras used for this study range from commercial grade to professional grade and included a GoPro Hero 1080 (5 Mp), iPhone 4S (8 Mp), Panasonic Lumix LX5 (9.5 Mp), Panasonic Lumix ZS20 (14.1 Mp) and Canon EOS 7D (18 Mp). The TLS used for this work was a FARO Focus 3D laser scanner with a range accuracy of ±2 mm. The study area is a small rock wall of about 6 m height and 20 m length. The wall is partly smooth with some evident geological features, such as non-persistent joints and sharp edges. Eight control points were placed on the wall and their coordinates were measured by using a total station. These coordinates were then used to georeference all models. A similar number of images was acquired from a distance of between approximately 5 to 10 m, depending on field of view of each camera. The commercial software package PhotoScan was used to process the images, georeference and scale the models, and to generate the dense point clouds. Finally, the open-source package CloudCompare was used to assess the accuracy of the multi-view results. Each point cloud obtained from a specific camera was compared to the point cloud obtained with the TLS. The latter is taken as ground truth. The result is a coloured point cloud for each camera showing the deviation in relation to the TLS data. The main goal of this study is to quantify the quality of the multi-view 3D reconstruction results obtained with various cameras as objectively as possible and to evaluate its applicability to geotechnical problems.

scholarly journals Dataset Denoising Based on Manifold Assumption

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhonghua Hao ◽  
Shiwei Ma ◽  
Hui Chen ◽  
Jingjing Liu
Keyword(s):  
Point Cloud ◽  
Geometric Distribution ◽  
Lagrange Equation ◽  
Optimal Solution ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Laplacian Operator ◽  
Discrete Laplacian ◽  
Denoising Method

Learning the knowledge hidden in the manifold-geometric distribution of the dataset is essential for many machine learning algorithms. However, geometric distribution is usually corrupted by noise, especially in the high-dimensional dataset. In this paper, we propose a denoising method to capture the “true” geometric structure of a high-dimensional nonrigid point cloud dataset by a variational approach. Firstly, we improve the Tikhonov model by adding a local structure term to make variational diffusion on the tangent space of the manifold. Then, we define the discrete Laplacian operator by graph theory and get an optimal solution by the Euler–Lagrange equation. Experiments show that our method could remove noise effectively on both synthetic scatter point cloud dataset and real image dataset. Furthermore, as a preprocessing step, our method could improve the robustness of manifold learning and increase the accuracy rate in the classification problem.

scholarly journals Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jingli Wang ◽  
Huiyuan Zhang ◽  
Jingxiang Gao ◽  
Dong Xiao
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Cloud Model ◽  
Hollow Structure ◽  
Total Least Squares ◽  
3D Point Cloud ◽  
Denoising Method ◽  
Least Squares Fitting ◽  
Cloud Models ◽  
Point Cloud Model

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.

scholarly journals Morphing and Sampling Network for Dense Point Cloud Completion

2020 ◽  
Vol 34 (07) ◽  
pp. 11596-11603 ◽  
Author(s):  
Minghua Liu ◽  
Lu Sheng ◽  
Sheng Yang ◽  
Jing Shao ◽  
Shi-Min Hu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Coarse Grained ◽  
Second Stage ◽  
The Earth ◽  
Novel Approach ◽  
Dense Point ◽  
Chamfer Distance ◽  
Structural Loss ◽  
Two Stages

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

scholarly journals A Denoising Method for LiDAR Full-Waveform Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xudong Lai ◽  
Min Zheng
Keyword(s):  
Pulse Width ◽  
Point Cloud ◽  
Mean Shift ◽  
Peak Position ◽  
Denoising Method ◽  
Positioning Accuracy ◽  
Waveform Data ◽  
Shift Method ◽  
Filtering Algorithm ◽  
Full Waveform

Decomposition of LiDAR full-waveform data can not only enhance the density and positioning accuracy of a point cloud, but also provide other useful parameters, such as pulse width, peak amplitude, and peak position which are important information for subsequent processing. Full-waveform data usually contain some random noises. Traditional filtering algorithms always cause distortion in the waveform.λ/μfiltering algorithm is based on Mean Shift method. It can smooth the signal iteratively and will not cause any distortion in the waveform. In this paper, an improvedλ/μfiltering algorithm is proposed, and several experiments on both simulated waveform data and real waveform data are implemented to prove the effectiveness of the proposed algorithm.

scholarly journals An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Shuhan Chen ◽  
Weiren Shi ◽  
Wenjie Zhang
Keyword(s):  
Noise Removal ◽  
Reference Image ◽  
Spatial Gradient ◽  
Signal Restoration ◽  
Classification Rate ◽  
Trilateral Filter ◽  
Mixed Noise ◽  
Mixed Noise Removal ◽  
Two Stages ◽  
Relative Differences

We propose a novel universal noise removal algorithm by combining spatial gradient and a new impulse statistic into the trilateral filter. By introducing a reference image, an impulse statistic is proposed, which is called directional absolute relative differences (DARD) statistic. Operation was carried out in two stages: getting reference image and image denoising. For denoising, we introduce the spatial gradient into the Gaussian filtering framework for Gaussian noise removal and integrate our DARD statistic for impulse noise removal, and finally we combine them together to create a new trilateral filter for mixed noise removal. Simulation results show that our noise detector has a high classification rate, especially for salt-and-pepper noise. And the proposed approach achieves great results both in terms of quantitative measures of signal restoration and qualitative judgments of image quality. In addition, the computational complexity of the proposed method is less than that of many other mixed noise filters.

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