Artículos de revistas: "Point cloud denoising"

1

Hou, Wenguang, Taiwai Chan y Mingyue Ding. "Denoising point cloud". Inverse Problems in Science and Engineering 20, n.º 3 (abril de 2012): 287–98. http://dx.doi.org/10.1080/17415977.2011.603087.

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2

Si, Shuming, Han Hu, Yulin Ding, Xuekun Yuan, Ying Jiang, Yigao Jin, Xuming Ge, Yeting Zhang, Jie Chen y Xiaocui Guo. "Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR". Remote Sensing 15, n.º 1 (2 de enero de 2023): 269. http://dx.doi.org/10.3390/rs15010269.

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Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on the sparsity assumption of point cloud noise, which does not hold for SPL point clouds, so the existing denoising methods cannot effectively remove the noisy points from SPL point clouds. To solve the above problems, we proposed a novel multistage denoising strategy with fused multiscale features. The multiscale features were fused to enrich contextual information of the point cloud at different scales. In addition, we utilized multistage denoising to solve the problem that a single-round denoising could not effectively remove enough noise points in some areas. Interestingly, the multiscale features also prevent an increase in false-alarm ratio during multistage denoising. The experimental results indicate that the proposed denoising approach achieved 97.58%, 99.59%, 95.70%, and 77.92% F1-scores in the urban, suburban, mountain, and water areas, respectively, and it outperformed the existing denoising methods such as Statistical Outlier Removal. The proposed approach significantly improved the denoising precision of airborne point clouds from single-photon LiDAR, especially in water areas and dense urban areas.

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3

ZHAO, Fu-qun. "Hierarchical point cloud denoising algorithm". Optics and Precision Engineering 28, n.º 7 (2020): 1618–25. http://dx.doi.org/10.37188/ope.20202807.1618.

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4

Liang, Xin He, Jin Liang y Chen Guo. "Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood". Advanced Materials Research 97-101 (marzo de 2010): 3631–36. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3631.

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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.

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5

Li, Zhiyuan, Jian Wang, Zhenyu Zhang, Fengxiang Jin, Juntao Yang, Wenxiao Sun y Yi Cao. "A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees". Remote Sensing 15, n.º 1 (25 de diciembre de 2022): 115. http://dx.doi.org/10.3390/rs15010115.

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Currently, the street tree resource survey using Mobile laser scanning (MLS) represents a hot spot around the world. Refined trunk extraction is an essential step for 3D reconstruction of street trees. However, due to scanning errors and the effects of occlusion by various types of features in the urban environment, street tree point cloud data processing has the problem of excessive noise. For the noise points that are difficult to remove using statistical methods in close proximity to the tree trunk, we propose an adaptive trunk extraction and denoising method for street trees based on an improved iForest (Isolation Forest) algorithm. Firstly, to extract the individual tree trunk points, the trunk and the crown are distinguished from the individual tree point cloud through point cloud slicing. Next, the iForest algorithm is improved by conducting automatic calculation of the contamination and further used to denoise the tree trunk point cloud. Finally, the method is validated with five datasets of different scenes. The results indicate that our method is robust and effective in extracting and denoising tree trunks. Compared with the traditional Statistical Outlier Removal (SOR) filter and Radius filter denoising methods, the denoising accuracy of the proposed method can be improved by approximately 30% for noise points close to tree trunks. Compared to iForest, the proposed method automatically calculates the contamination, improving the automation of the algorithm. Our method can provide more precise trunk point clouds for 3D reconstruction of street trees.

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6

Leal, Esmeide, German Sanchez-Torres y John W. Branch. "Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement". Sensors 20, n.º 11 (5 de junio de 2020): 3206. http://dx.doi.org/10.3390/s20113206.

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Denoising the point cloud is fundamental for reconstructing high quality surfaces with details in order to eliminate noise and outliers in the 3D scanning process. The challenges for a denoising algorithm are noise reduction and sharp features preservation. In this paper, we present a new model to reconstruct and smooth point clouds that combine L1-median filtering with sparse L1 regularization for both denoising the normal vectors and updating the position of the points to preserve sharp features in the point cloud. The L1-median filter is robust to outliers and noise compared to the mean. The L1 norm is a way to measure the sparsity of a solution, and applying an L1 optimization to the point cloud can measure the sparsity of sharp features, producing clean point set surfaces with sharp features. We optimize the L1 minimization problem by using the proximal gradient descent algorithm. Experimental results show that our approach is comparable to the state-of-the-art methods, as it filters out 3D models with a high level of noise, but keeps their geometric features.

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7

Nex, F. y M. Gerke. "Photogrammetric DSM denoising". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (11 de agosto de 2014): 231–38. http://dx.doi.org/10.5194/isprsarchives-xl-3-231-2014.

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Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers. These problems constitute a limitation in the practical use of photogrammetric data for many applications but an effective way to enhance the generated point cloud has still to be found. In this paper we concentrate on the restoration of Digital Surface Models (DSM), computed from dense image matching point clouds. A photogrammetric DSM, i.e. a 2.5D representation of the surface is still one of the major products derived from point clouds. Four different algorithms devoted to DSM denoising are presented: a standard median filter approach, a bilateral filter, a variational approach (TGV: Total Generalized Variation), as well as a newly developed algorithm, which is embedded into a Markov Random Field (MRF) framework and optimized through graph-cuts. The ability of each algorithm to recover the original DSM has been quantitatively evaluated. To do that, a synthetic DSM has been generated and different typologies of noise have been added to mimic the typical errors of photogrammetric DSMs. The evaluation reveals that standard filters like median and edge preserving smoothing through a bilateral filter approach cannot sufficiently remove typical errors occurring in a photogrammetric DSM. The TGV-based approach much better removes random noise, but large areas with outliers still remain. Our own method which explicitly models the degradation properties of those DSM outperforms the others in all aspects.

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8

Mugner, E. y N. Seube. "DENOISING OF 3D POINT CLOUDS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (29 de noviembre de 2019): 217–24. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-217-2019.

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Abstract. A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system may supplement a geometric approach. Our method can be applied to any point cloud, acquired by a fixed, a mobile or an airborne LiDAR system. We present the principle of the method and some results from various LiDAR system mounted on UAVs. A comparison of a low-cost LiDAR system and a high-grade LiDAR system is performed on the same area, showing the benefits of applying our denoising algorithm to UAV LiDAR data. We also present the impact of denoising as a pre-processing tool for ground classification applications. Finaly, we also show some application of our denoising algorithm to dense point clouds produced by a photogrammetry software.

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9

Liu, Q., X. Jin, Y. J. Yang, Y. C. Zou y W. Zhang. "Optimization of point cloud preprocessing algorithm for equipped vehicles". Journal of Physics: Conference Series 2383, n.º 1 (1 de diciembre de 2022): 012080. http://dx.doi.org/10.1088/1742-6596/2383/1/012080.

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In view of the fact that the point cloud 3D model will be interfered by environmental factors, measurement methods and other random factors in the process of data scanning and acquisition, there will be some invalid points, outliers and internal noise points. In this paper, a point cloud denoising method based on adaptive density clustering and statistical filtering is proposed to process vehicle point cloud data. which can effectively preserve vehicle features while obtaining optimal denoising effect. Compared with the existing point cloud noise processing algorithms, this algorithm can remove noise better, and has shorter time-consuming and good applicability.

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10

CUI Xin, 崔. 鑫., 闫秀天 YAN Xiu-tian y 李世鹏 LI Shi-peng. "Feature-preserving scattered point cloud denoising". Optics and Precision Engineering 25, n.º 12 (2017): 3169–78. http://dx.doi.org/10.3788/ope.20172512.3169.

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11

Mattei, E. y A. Castrodad. "Point Cloud Denoising via Moving RPCA". Computer Graphics Forum 36, n.º 8 (2 de noviembre de 2016): 123–37. http://dx.doi.org/10.1111/cgf.13068.

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12

Rosman, G., A. Dubrovina y R. Kimmel. "Patch-Collaborative Spectral Point-Cloud Denoising". Computer Graphics Forum 32, n.º 8 (3 de junio de 2013): 1–12. http://dx.doi.org/10.1111/cgf.12139.

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13

Zhao, Can, Yun Ling Shi y Jun Ting Cheng. "A New near Point Denoising Algorithm for Point Cloud". Advanced Materials Research 479-481 (febrero de 2012): 2152–56. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.2152.

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For the mass point cloud, which is generated by large work piece for free surface, the point cloud noise removal is the most important step, after denoising point cloud which quality will directly influence the follow-up point normal vector estimation and curvature estimation. Therefore, this paper presents a simple and efficient algorithm for near-point denoising. Firstly, it using the bounding box for messy point cloud data differentiate space topology structure, then, traverse all points, for each point looking for its K neighborhood, and fitting quadric surface using the K neighbors; finally, using Z value method for calculation of the distance that point to the secondary surface distance. Setting threshold, and if the distance beyond the threshold, then the point that noise points and delete. Experiments show that this algorithm compared with the traditional algorithm, not only improve efficiency, and can be a very good retain the original model data, but also for the follow-up process provides high quality raw data, there is a wide useful in three dimensions scanning, projective measurement reverse design and other fields.

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14

Zhu, Ming, Biao Leng, Chunhong Xiao, Gaopeng Hou, Xiangchen Yao y Kai Li. "Research on Fast Pre-Processing Method of Tunnel Point Cloud Data in Complex Environment". Journal of Physics: Conference Series 2185, n.º 1 (1 de enero de 2022): 012038. http://dx.doi.org/10.1088/1742-6596/2185/1/012038.

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Abstract In recent years, the application and research of three-dimensional laser scanning technology in tunnel engineering has continuously emerged, while the shortcomings still exists. This paper conducts research on quick preprocessing method of tunnel point cloud data based on three-dimensional laser scanning. For streamlining the tunnel point cloud model data, comparison analysis discusses are applied to both the downsampling algorithm and the point cloud denoising method respectively. Simulation experiments are introduced to compare the effects of different downsampling algorithms applied to the tunnel point cloud model, analyze the sampling efficiency and performance of each algorithm. Combining with statistical denoising and radius denoising algorithms, a distance denoising based on an iterative filtering model method is proposed. The result shows that this method is suitable for the tunnel point cloud model during the construction period with complex environment, and can effectively eliminate most of the point cloud noise.

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15

Liu, Youyu, Baozhu Zou, Jiao Xu, Siyang Yang y Yi Li. "Denoising for 3D Point Cloud Based on Regularization of a Statistical Low-Dimensional Manifold". Sensors 22, n.º 7 (30 de marzo de 2022): 2666. http://dx.doi.org/10.3390/s22072666.

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A point cloud obtained by stereo matching algorithm or three-dimensional (3D) scanner generally contains much complex noise, which will affect the accuracy of subsequent surface reconstruction or visualization processing. To eliminate the complex noise, a new regularization algorithm for denoising was proposed. In view of the fact that 3D point clouds have low-dimensional structures, a statistical low-dimensional manifold (SLDM) model was established. By regularizing its dimensions, the denoising problem of the point cloud was expressed as an optimization problem based on the geometric constraints of the regularization term of the manifold. A low-dimensional smooth manifold model was constructed by discrete sampling, and solved by means of a statistical method and an alternating iterative method. The performance of the denoising algorithm was quantitatively evaluated from three aspects, i.e., the signal-to-noise ratio (SNR), mean square error (MSE) and structural similarity (SSIM). Analysis and comparison of performance showed that compared with the algebraic point-set surface (APSS), non-local denoising (NLD) and feature graph learning (FGL) algorithms, the mean SNR of the point cloud denoised using the proposed method increased by 1.22 DB, 1.81 DB and 1.20 DB, respectively, its mean MSE decreased by 0.096, 0.086 and 0.076, respectively, and its mean SSIM decreased by 0.023, 0.022 and 0.020, respectively, which shows that the proposed method is more effective in eliminating Gaussian noise and Laplace noise in common point clouds. The application cases showed that the proposed algorithm can retain the geometric feature information of point clouds while eliminating complex noise.

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16

Hui, Z., P. Cheng, L. Wang, Y. Xia, H. Hu y X. Li. "A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (5 de junio de 2019): 1021–25. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1021-2019.

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Abstract. Denoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based on empirical mode decomposition to remove outliers from airborne LiDAR point cloud. Comparing with traditional point cloud denoising algorithms, the proposed method can detect outliers from a signal processing perspective. Firstly, airborne LiDAR point clouds are decomposed into a series of intrinsic mode functions with the help of morphological operations, which would significantly decrease the computational complexity. By applying OTSU algorithm to these intrinsic mode functions, noise-dominant components can be detected and filtered. Finally, outliers are detected automatically by comparing observed elevations and reconstructed elevations. Three datasets located at three different cities in China were used to verify the validity and robustness of the proposed method. The experimental results demonstrate that the proposed method removes both high and low outliers effectively with various terrain features while preserving useful ground details.

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17

Dong, Ting Jian, Hua Peng Ding, Tao Wang, Hao Wang y Jin Chen. "Adaptive Denoising Algorithm for Scanning Beam Points Based on Angle Thresholds". Applied Mechanics and Materials 741 (marzo de 2015): 204–8. http://dx.doi.org/10.4028/www.scientific.net/amm.741.204.

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A local adaptive neighborhood model is proposed in this paper in order to deal with the mistake judgment in the existing scanning beam point cloud denoising algorithms. Such a model regards larger curvatures as the potential noises, can select angle thresholds of noise points and the median values of filtering windows adaptively, so as address the issues of mistake judgment and missing judgment of the point clouds denoising algorithms with different curvatures. The adaption theory in the angle threshold denoising algorithm classifies the noise points and data points. Therefore, it can ensure the smoothness in low frequency, and as well keep the high frequency characteristics. The new method improves the accuracy of median filtering, prevents the diffusion of noise, remove noises effectively while preserving sharp features, and avoid fuzzy data margin.

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18

Xu, Xueli, Guohua Geng, Xin Cao, Kang Li y Mingquan Zhou. "TDNet: transformer-based network for point cloud denoising". Applied Optics 61, n.º 6 (15 de diciembre de 2021): C80. http://dx.doi.org/10.1364/ao.438396.

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19

Zhou, Yiyao, Rui Chen, Yiqiang Zhao, Xiding Ai y Guoqing Zhou. "Point cloud denoising using non-local collaborative projections". Pattern Recognition 120 (diciembre de 2021): 108128. http://dx.doi.org/10.1016/j.patcog.2021.108128.

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20

Dinesh, Chinthaka, Gene Cheung y Ivan V. Bajic. "Point Cloud Denoising via Feature Graph Laplacian Regularization". IEEE Transactions on Image Processing 29 (2020): 4143–58. http://dx.doi.org/10.1109/tip.2020.2969052.

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21

Zheng, Yinglong, Guiqing Li, Shihao Wu, Yuxin Liu y Yuefang Gao. "Guided point cloud denoising via sharp feature skeletons". Visual Computer 33, n.º 6-8 (10 de mayo de 2017): 857–67. http://dx.doi.org/10.1007/s00371-017-1391-8.

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22

Liu, Zheng, Xiaowen Xiao, Saishang Zhong, Weina Wang, Yanlei Li, Ling Zhang y Zhong Xie. "A feature-preserving framework for point cloud denoising". Computer-Aided Design 127 (octubre de 2020): 102857. http://dx.doi.org/10.1016/j.cad.2020.102857.

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23

Hu, Wei, Xiang Gao, Gene Cheung y Zongming Guo. "Feature Graph Learning for 3D Point Cloud Denoising". IEEE Transactions on Signal Processing 68 (2020): 2841–56. http://dx.doi.org/10.1109/tsp.2020.2978617.

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24

LONG, Jia-le, Zi-hao DU, Jian-min ZHANG, Fu-jian CHEN, Hao-yuan GUAN, Ke-sen HUANG y Rui SUN. "Point cloud denoising method based on image segmentation". Chinese Journal of Liquid Crystals and Displays 38, n.º 1 (2023): 104–17. http://dx.doi.org/10.37188/cjlcd.2022-0171.

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25

Guo, Jiao, Ke Li y Hao Xu. "A Local Enhancement Method for Large-Scale Building Facade Depth Images using Densely Matched Point Clouds". Advances in Multimedia 2022 (12 de agosto de 2022): 1–11. http://dx.doi.org/10.1155/2022/3175998.

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In recent years, laser scanning systems have been widely used to acquire multi-level three-dimensional spatial objects in real time. The laser scanning system is used to acquire the three-dimensional point cloud data of urban scenes. Due to the large-scale characteristics of urban scenes, and the problems of scanning occlusion, scanning path, and limited scanning laser range, the laser scanning system cannot scan every object in the scene comprehensively, multidirectionally and finely, so the corresponding three-dimensional point cloud data collected by many objects are incomplete, and the data images are relatively sparse and unevenly distributed. The existing point cloud denoising and enhancement algorithms, such as AMLS, RMLS, LOP, and WLOP, all use local information to enhance the missing or sparse parts of the point cloud. This point cloud enhancement method is only limited to a small range and cannot do anything for the larger missing area of the point cloud. Even if it is done reluctantly, the effect is not satisfactory. There are a lot of repetitive and similar features in urban buildings, such as the repetitive areas of floors and balconies in buildings. These repetitive areas are distributed in different positions of point clouds, so the repetitive information has non local characteristics. Based on the nonlocal characteristics of building point cloud data and the repetitive structure of buildings, this article proposes a nonlocal point cloud data enhancement algorithm, which organizes the point cloud data in the repeated area into a set of basic geometric elements (planes). The structures are registered in a unified coordinate system, and the point cloud is enhanced and denoised through two denoising processes, “out-of-plane” and “in-plane.”

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Wang, Jingli, Huiyuan Zhang, Jingxiang Gao y Dong Xiao. "Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM". Computational Intelligence and Neuroscience 2021 (13 de septiembre de 2021): 1–8. http://dx.doi.org/10.1155/2021/9927982.

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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.

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Irfan, Muhammad Abeer y Enrico Magli. "Exploiting color for graph-based 3D point cloud denoising". Journal of Visual Communication and Image Representation 75 (febrero de 2021): 103027. http://dx.doi.org/10.1016/j.jvcir.2021.103027.

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Hu, Wei, Qianjiang Hu, Zehua Wang y Xiang Gao. "Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance". IEEE Transactions on Image Processing 30 (2021): 6168–83. http://dx.doi.org/10.1109/tip.2021.3092826.

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Chen, Honghua, Zeyong Wei, Xianzhi Li, Yabin Xu, Mingqiang Wei y Jun Wang. "RePCD-Net: Feature-Aware Recurrent Point Cloud Denoising Network". International Journal of Computer Vision 130, n.º 3 (17 de enero de 2022): 615–29. http://dx.doi.org/10.1007/s11263-021-01564-7.

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30

Yan, Chuang, Ya Wei, Yong Xiao y Linbing Wang. "Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection". Sensors 21, n.º 7 (25 de marzo de 2021): 2310. http://dx.doi.org/10.3390/s21072310.

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As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement.

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31

Huo, Kai, Hui Yang, Hairong Fang, Shijian Shi y Yufei Chen. "Simplification of point cloud data for large-scale ellipsoidal complex surface". Journal of Physics: Conference Series 2029, n.º 1 (1 de septiembre de 2021): 012061. http://dx.doi.org/10.1088/1742-6596/2029/1/012061.

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Abstract Aiming at the issue that the current point cloud data simplification algorithm cannot accurately retain the subtle features of ellipsoidal complex surface, simplification algorithm of a point cloud data feature preservation based on principle component analysis (PCA) and adaptive mean shift (AMS) methods is proposed. First, K-means spatial clustering is used to make preliminary clustering and simplification of point cloud data. Second, the representative value of the subtle features of the point cloud data is calculated based on the PCA method. Then, according to the proposed improved adaptive mean shift method, the simplification of the point cloud data is developed. Finally, the point cloud data obtained by the denoising and smoothing of a large spherical crown workpiece collected at a certain equipment manufacturing site is used as the experimental object, and a comparative experiment is conducted between the proposed simplification algorithm and the k-means clustering algorithm, which proves the proposed algorithm can effectively maintain the subtle feature points of the ellipsoidal point cloud data.

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32

Yang, Zeyin. "3D Modeling of Sculpture Nano-Ceramics under Sparse Image Sequence". International Journal of Analytical Chemistry 2022 (7 de julio de 2022): 1–8. http://dx.doi.org/10.1155/2022/5710535.

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To improve the analysis ability of point cloud 3D reconstruction of sparse images of nano-ceramic sculpture points, an automatic cloud 3D reconstruction method of nano-ceramic sculpture points based on sparse image sequence is proposed. Firstly, 3D angle detection and edge contour feature extraction methods are used to analyze 3D point cloud features of nano-ceramic sculpture point save image; secondly, the point cloud of the fuel economy image of nano-ceramic sculpture points is merged and the sloping action method is used to shape degradation to realize the information increase and fusion filtering of the fuel economy image of nano-ceramic sculpture points; finally, combined with the local mean denoising method, image is refined to improve the ability of sparse image outline structure of nano-ceramic sculpture points. The simulation results show that this method has high accuracy, good image matching ability, and high signal-to-noise ratio.

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33

Yan, Jieqiong, Laishui Zhou, Jun Wang, Xiaoping Wang y Xia Liu. "Structural Feature-Preserving Point Cloud Denoising Method for Aero-Engine Profile". International Journal of Aerospace Engineering 2022 (16 de marzo de 2022): 1–24. http://dx.doi.org/10.1155/2022/9565062.

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The ex-service and old type aero-engines are valuable for education. In many cases, these aero-engines only have physical objects, but lack geometric models. This brings difficulties to talent cultivation. Therefore, the education department needs to reconstruct geometric models of above aero-engines. The laser scanning devices provide raw data of aero-engine profile, but noise directly affects reconstruction accuracy. In order to ensure that noise is removed without blurring or distorting structural features, a structural feature-preserving point cloud denoising method is proposed. The noisy point cloud is divided into casing feature data, pipeline feature data and complex shape feature data. According to shape characteristics of each feature data, three denoising networks are designed to estimate position correction vectors of noisy points and project them back onto underlying surfaces. Qualitative and quantitative experiments show that our method significantly outperforms state-of-the-art methods, both in terms of preservation and restoration of structural features.

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34

Cheng, Dongyang, Dangjun Zhao, Junchao Zhang, Caisheng Wei y Di Tian. "PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data". Sensors 21, n.º 11 (26 de mayo de 2021): 3703. http://dx.doi.org/10.3390/s21113703.

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Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

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35

Shi, Chunhao, Chunyang Wang, Xuelian Liu, Shaoyu Sun, Bo Xiao, Xuemei Li y Guorui Li. "Three-dimensional point cloud denoising via a gravitational feature function". Applied Optics 61, n.º 6 (10 de febrero de 2022): 1331. http://dx.doi.org/10.1364/ao.446913.

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36

Cao, Feifei. "An Algorithm of Anisotropy and Self-adaptive Point Cloud Denoising". Journal of Information and Computational Science 11, n.º 8 (20 de mayo de 2014): 2827–34. http://dx.doi.org/10.12733/jics20103697.

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37

Li Renzhong, 李仁忠, 杨曼 Yang Man, 冉媛 Ran Yuan, 张缓缓 Zhang Huanhuan, 景军锋 Jing Junfeng y 李鹏飞 Li Pengfei. "Point Cloud Denoising and Simplification Algorithm Based on Method Library". Laser & Optoelectronics Progress 55, n.º 1 (2018): 011008. http://dx.doi.org/10.3788/lop55.011008.

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38

Zhang, Xue, Gene Cheung, Jiahao Pang, Yash Sanghvi, Abhiram Gnanasambandam y Stanley H. Chan. "Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement". IEEE Transactions on Image Processing 31 (2022): 6863–78. http://dx.doi.org/10.1109/tip.2022.3214077.

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39

Yang, Yang, Ming Li y Xie Ma. "An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data". Applied Sciences 10, n.º 15 (4 de agosto de 2020): 5379. http://dx.doi.org/10.3390/app10155379.

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To further improve the efficiency and accuracy of the vehicle part inspection process, this paper designs an accurate and efficient vehicle body part inspection framework based on scattered point cloud data (PCD). Firstly, a hybrid filtering algorithm for point cloud denoising is designed to solve the problem of multiple noise points in the original point cloud measurement data. Secondly, a point cloud simplification algorithm based on Fuzzy C-Means (FCM) is designed to solve the problems of a large amount of data and many redundant points in the PCD. Thirdly, a point cloud fine registration algorithm based on the Teaching-Learning-based Optimization (TLBO) algorithm is designed to solve the problem where the initial point cloud measurement data cannot be located properly. Finally, the deviation distance between the PCD and Computer-Aided-Design (CAD) model is calculated by the K-Nearest Neighbor (KNN) algorithm to inspect and analyze the point cloud after preprocessing. On the basis of the design algorithm, four groups that contain measurement data for eight vehicle body parts are analyzed and the results prove the effectiveness of the algorithm, which is very suitable for the inspection process of vehicle body parts.

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40

Li, Xingdong, Zhiming Gao, Xiandong Chen, Shufa Sun y Jiuqing Liu. "Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud". Information 12, n.º 6 (30 de mayo de 2021): 235. http://dx.doi.org/10.3390/info12060235.

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A single VLP-16 LiDAR estimation method based on a single-frame 3D laser point cloud is proposed to address the problem of estimating negative obstacles’ geometrical features in structured environments. Firstly, a distance measurement method is developed to determine the estimation range of the negative obstacle, which can be used to verify the accuracy of distance estimation. Secondly, the 3D point cloud of a negative obstacle is transformed into a 2D elevation raster image, making the detection and estimation of negative obstacles more intuitive and accurate. Thirdly, we compare the effects of a StatisticalOutlierRemoval filter, RadiusOutlier removal, and Conditional removal on 3D point clouds, and the effects of a Gauss filter, Median filter, and Aver filter on 2D image denoising, and design a flowchart for point cloud and image noise reduction and denoising. Finally, a geometrical feature estimation method is proposed based on the elevation raster image. The negative obstacle image in the raster is used as an auxiliary line, and the number of pixels is derived from the OpenCV-based Progressive Probabilistic Hough Transform to estimate the geometrical features of the negative obstacle based on the raster size. The experimental results show that the algorithm has high accuracy in estimating the geometric characteristics of negative obstacles on structured roads and has a practical application value for LiDAR environment perception research.

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41

Zhang, Liming, Lei Wang, Xu du y Fanbo Meng. "CAD-Aided 3D Reconstruction of Intelligent Manufacturing Image Based on Time Series". Scientific Programming 2022 (11 de marzo de 2022): 1–11. http://dx.doi.org/10.1155/2022/9022563.

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To improve the three-dimensional (3D) reconstruction effect of intelligent manufacturing image and reduce the reconstruction time, a new CAD-aided 3D reconstruction of intelligent manufacturing image based on time series was proposed. Kinect sensor is used to collect depth image data and convert it into 3D point cloud coordinates. The collected point cloud data are divided into regions, and different point cloud denoising algorithms are used to filter and denoise the divided regions. With the help of CAD, FLANN matching algorithm is used to extract feature points of time-series images and complete image matching. Three-dimensional reconstruction of sparse point cloud and dense point cloud is carried out to complete 3D reconstruction of intelligent manufacturing images. The experimental results show that the image PSNR of this method is always above 52 dB, and the maximum reconstruction time is 4.9 s. The 3D reconstruction effect of intelligent manufacturing image is better, and it has higher practical application value.

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42

Hao, Zhonghua, Shiwei Ma, Hui Chen y Jingjing Liu. "Dataset Denoising Based on Manifold Assumption". Mathematical Problems in Engineering 2021 (18 de enero de 2021): 1–14. http://dx.doi.org/10.1155/2021/6432929.

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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.

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43

Zhu, Guokai, Chunmei Li y Yuexi Zhong. "Point cloud registration method based on Particle Swarm Optimization algorithm and improved ICP". Journal of Physics: Conference Series 2395, n.º 1 (1 de diciembre de 2022): 012078. http://dx.doi.org/10.1088/1742-6596/2395/1/012078.

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Abstract In order to achieve accurate wheel gripping by truss manipulators, accurate point cloud alignment of train wheels is required. In this paper, the wheels are detected by binocular laser displacement sensors. And accurate point cloud alignment is achieved by using the point cloud denoising and streamlining algorithm, particle swarm-based algorithm, and improved ICP algorithm for 3D point cloud data. The experimental results show that reducing noise and data volume can improve the stitching effect and alignment efficiency of 3D point cloud models. The algorithm of this paper is experimentally verified to be 43% better in error accuracy and 93% faster in time-consuming than the traditional ICP algorithm, which verifies the effectiveness of its method.

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44

Cao Xiong, 曹雄, 林兆祥 Lin Zhaoxiang, 宋沙磊 Song Shalei, 王滨辉 Wang Binhui, 何东 He Dong y 刘中正 Liu Zhongzheng. "基于颜色聚类的多光谱激光雷达点云去噪". Laser & Optoelectronics Progress 58, n.º 12 (2021): 1228002. http://dx.doi.org/10.3788/lop202158.1228002.

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45

Liu, Chun, Meijing Guang y Shanshan Yu. "Point cloud and BIM model registration based on genetic algorithm and ICP algorithm". Journal of Physics: Conference Series 2132, n.º 1 (1 de diciembre de 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2132/1/012007.

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Abstract With the rapid development of the construction industry, BIM technology, and 3D laser scanning technology are being used more and more widely, and there are many applications of combining BIM technology with 3D laser scanning technology, such as quality inspection, progress inspection, or digital preservation of ancient buildings. Therefore, this paper proposes a 3D point cloud and BIM model registration scheme based on genetic algorithm and ICP algorithm, firstly, the point cloud data is pre-processed by statistical denoising method for denoising and downsampling, and the BIM model data is converted to format data; then the coarse registration is performed by genetic algorithm, and the accurate registration is performed by ICP algorithm based on KD-tree, and finally, we experimentally verify the feasibility of the algorithm in this paper, and compared with the ICP algorithm, the registration efficiency and accuracy of the algorithm in this paper are greatly improved.

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46

Irfan, Muhammad Abeer y Enrico Magli. "Joint Geometry and Color Point Cloud Denoising Based on Graph Wavelets". IEEE Access 9 (2021): 21149–66. http://dx.doi.org/10.1109/access.2021.3054171.

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47

Zhou, Lang, Guoxing Sun, Yong Li, Weiqing Li y Zhiyong Su. "Point cloud denoising review: from classical to deep learning-based approaches". Graphical Models 121 (mayo de 2022): 101140. http://dx.doi.org/10.1016/j.gmod.2022.101140.

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48

Zhu, Dingkun, Honghua Chen, Weiming Wang, Haoran Xie, Gary Cheng, Mingqiang Wei, Jun Wang y Fu Lee Wang. "Nonlocal Low-Rank Point Cloud Denoising for 3-D Measurement Surfaces". IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–14. http://dx.doi.org/10.1109/tim.2021.3139686.

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49

Zhao Kai, 赵凯, 徐友春 Xu Youchun, 李永乐 Li Yongle y 王任栋 Wang Rendong. "Large-Scale Scattered Point-Cloud Denoising Based on VG-DBSCAN Algorithm". Acta Optica Sinica 38, n.º 10 (2018): 1028001. http://dx.doi.org/10.3788/aos201838.1028001.

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50

Yong-hua, Shan, Zhang Xu-qing, Niu Xue-feng, Yang guo-dong y Zhang Ji-Kai. "Denoising Algorithm of Airborne LIDAR Point Cloud Based on 3D Grid". International Journal of Signal Processing, Image Processing and Pattern Recognition 10, n.º 3 (31 de marzo de 2017): 85–92. http://dx.doi.org/10.14257/ijsip.2017.10.3.09.

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