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Готові списки джерел за темами / Point cloud denoising

Добірка наукової літератури з теми "Point cloud denoising"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Point cloud denoising"

1

Hou, Wenguang, Taiwai Chan, and Mingyue Ding. "Denoising point cloud." Inverse Problems in Science and Engineering 20, no. 3 (April 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, and Xiaocui Guo. "Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR." Remote Sensing 15, no. 1 (January 2, 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, no. 7 (2020): 1618–25. http://dx.doi.org/10.37188/ope.20202807.1618.

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4

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

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Анотація:

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, and Yi Cao. "A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees." Remote Sensing 15, no. 1 (December 25, 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, and John W. Branch. "Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement." Sensors 20, no. 11 (June 5, 2020): 3206. http://dx.doi.org/10.3390/s20113206.

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Анотація:

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

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Анотація:

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

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Анотація:

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, and W. Zhang. "Optimization of point cloud preprocessing algorithm for equipped vehicles." Journal of Physics: Conference Series 2383, no. 1 (December 1, 2022): 012080. http://dx.doi.org/10.1088/1742-6596/2383/1/012080.

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Анотація:

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, and 李世鹏 LI Shi-peng. "Feature-preserving scattered point cloud denoising." Optics and Precision Engineering 25, no. 12 (2017): 3169–78. http://dx.doi.org/10.3788/ope.20172512.3169.

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Більше джерел

Дисертації з теми "Point cloud denoising"

1

Lee, Kai-wah, and 李啟華. "Mesh denoising and feature extraction from point cloud data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664330.

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2

Lee, Kai-wah. "Mesh denoising and feature extraction from point cloud data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664330.

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3

IRFAN, MUHAMMAD ABEER. "Joint geometry and color denoising for 3D point clouds." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912976.

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Частини книг з теми "Point cloud denoising"

1

Zaman, Faisal, Ya Ping Wong, and Boon Yian Ng. "Density-Based Denoising of Point Cloud." In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, 287–95. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1721-6_31.

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2

Pistilli, Francesca, Giulia Fracastoro, Diego Valsesia, and Enrico Magli. "Learning Graph-Convolutional Representations for Point Cloud Denoising." In Computer Vision – ECCV 2020, 103–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_7.

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3

Li, Yushi, and George Baciu. "PC-OPT: A SfM Point Cloud Denoising Algorithm." In Lecture Notes in Computer Science, 280–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62362-3_25.

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4

Mao, Aihua, Zihui Du, Yu-Hui Wen, Jun Xuan, and Yong-Jin Liu. "PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows." In Lecture Notes in Computer Science, 398–415. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20062-5_23.

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5

Siyong, Fu, and Wu Lushen. "Denoising Point Cloud Based on the Connexity of Spatial Grid." In Advances in Intelligent Systems and Computing, 12–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00214-5_2.

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6

Castillo, Edward, Jian Liang, and Hongkai Zhao. "Point Cloud Segmentation and Denoising via Constrained Nonlinear Least Squares Normal Estimates." In Mathematics and Visualization, 283–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34141-0_13.

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7

Morales, Rixio, Yunhong Wang, and Zhaoxiang Zhang. "Unstructured Point Cloud Surface Denoising and Decimation Using Distance RBF K-Nearest Neighbor Kernel." In Advances in Multimedia Information Processing - PCM 2010, 214–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15696-0_20.

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8

Chen, Lei, Yuan Yuan, and Shide Song. "Hierarchical Denoising Method of Crop 3D Point Cloud Based on Multi-view Image Reconstruction." In Computer and Computing Technologies in Agriculture XI, 416–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06137-1_38.

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Тези доповідей конференцій з теми "Point cloud denoising"

1

Gao, Xianz, Wei Hu, and Zongming Guo. "Graph-Based Point Cloud Denoising." In 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM). IEEE, 2018. http://dx.doi.org/10.1109/bigmm.2018.8499090.

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2

Luo, Shitong, and Wei Hu. "Score-Based Point Cloud Denoising." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00454.

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3

Huang, Wenming, Yuanwang Li, Peizhi Wen, and Xiaojun Wu. "Algorithm for 3D Point Cloud Denoising." In 2009 Third International Conference on Genetic and Evolutionary Computing (WGEC 2009). IEEE, 2009. http://dx.doi.org/10.1109/wgec.2009.139.

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4

Almonacid, Jonathan, Celia Cintas, Claudio Derieux, and Mirtha Lewis. "Point Cloud Denoising using Deep Learning." In 2018 Argentine Congress of Computer Science and Research Development (CACIDI). IEEE, 2018. http://dx.doi.org/10.1109/cacidi.2018.8584185.

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5

Luo, Shitong, and Wei Hu. "Differentiable Manifold Reconstruction for Point Cloud Denoising." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413727.

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6

Shang, Xin, Rong Ye, Hui Feng, and Xueqin Jiang. "Robust Feature Graph for Point Cloud Denoising." In 2022 7th International Conference on Communication, Image and Signal Processing (CCISP). IEEE, 2022. http://dx.doi.org/10.1109/ccisp55629.2022.9974370.

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7

Smítka, Václav, and Martin Štroner. "3D scanner point cloud denoising by near points surface fitting." In SPIE Optical Metrology 2013, edited by Fabio Remondino, Mark R. Shortis, Jürgen Beyerer, and Fernando Puente León. SPIE, 2013. http://dx.doi.org/10.1117/12.2020254.

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8

Xing, Jinrui, Hui Yuan, Chen Chen, and Tian Guo. "Wiener Filter-Based Point Cloud Adaptive Denoising for Video-based Point Cloud Compression." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3552457.3555733.

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9

Phuong, Nguyen Thi Huu, Dang Van Duc, and Nguyen Truong Xuan. "LIDAR POINT CLOUD DENOISING METHOD USING NOMINAL POINT SPACING (NPS)." In THE 14TH NATIONAL CONFERENCE ON FUNDAMENTAL AND APPLIED INFORMATION TECHNOLOGY RESEARCH. Nhà xuất bản Khoa học tự nhiên và Công nghệ, 2021. http://dx.doi.org/10.15625/vap.2021.0073.

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10

Li, Xiaozhi, and Xiaojiu Li. "3D Body Point Cloud Data Denoising and Registration." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.376.

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