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Low Rank Matrix Approximation for Geometry Filtering [article]

Xuequan Lu, Scott Schaefer, Jun Luo, Lizhuang Ma, Ying He
2018 arXiv   pre-print
We propose a robust normal estimation method for both point clouds and meshes using a low rank matrix approximation algorithm.  ...  We then show that a low rank matrix approximation algorithm can robustly estimate normals for both point clouds and meshes.  ...  Nonlocal Methods for Point Clouds and Nuclear Norm Minimization Previous researchers proposed non-local methods for point clouds. For example, Zheng et al.  ... 
arXiv:1803.06783v2 fatcat:5ckabbt2tbba7nfcwjs6u3wgrq

Rethinking Point Cloud Filtering: A Non-Local Position Based Approach [article]

Jinxi Wang, Jincen Jiang, Xuequan Lu, Meili Wang
2021 arXiv   pre-print
In this paper, we rethink point cloud filtering from a non-learning non-local non-normal perspective, and propose a novel position based approach for feature-preserving point cloud filtering.  ...  Existing position based point cloud filtering methods can hardly preserve sharp geometric features.  ...  [23] designed a local algorithm for 3D point cloud denoising.  ... 
arXiv:2110.07253v1 fatcat:q5hdvqds2rbfxkcdfcbd367wza

3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model [article]

Jin Zeng, Gene Cheung, Michael Ng, Jiahao Pang, Cheng Yang
2019 arXiv   pre-print
In this paper, we adopt a previously proposed low-dimensional manifold model for the surface patches in the point cloud and seek self-similar patches to denoise them simultaneously using the patch manifold  ...  3D point cloud - a new signal representation of volumetric objects - is a discrete collection of triples marking exterior object surface locations in 3D space.  ...  . , v N ] ∈ R N ×3 be the position matrix for the point cloud.  ... 
arXiv:1803.07252v2 fatcat:3i3ir7fyorehjkocbqssqzkgx4

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
Wu, Y., Zheng, G., Zhang, D., Zhang, Y., and Li, Y., Tropical Cyclone Center  ...  ., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T.,  ...  ., +, TGRS Feb. 2019 866-880 Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation [article]

Na Liu, Wei Li, Yinjian Wang, Rao Tao, Qian Du, Jocelyn Chanussot
2022 arXiv   pre-print
Compared to low-rank matrix approximation (LRMA), LRTA is capable of characterizing more complex intrinsic structure of high-order data and owns more efficient learning abilities, being established to  ...  Low-rank tensor approximation (LRTA) is such an emerging technique, having gained much attention in HSI restoration community, with ever-growing theoretical foundation and pivotal technological innovation  ...  Acknowledgements This work was supported by the National Key R&D Program of China (Grant No. 2021YFB3900502), the China Postdoctoral Science Foundation (Grant no. 2021M700440), the National Natural Science  ... 
arXiv:2205.08839v1 fatcat:34i56hcmvbbt7bs77tsghpuvoi

Table of contents

2020 IEEE Transactions on Geoscience and Remote Sensing  
Verbeeck 3057 TGNet: Geometric Graph CNN on 3-D Point Cloud Segmentation ........ Y. Li, L. Ma, Z. Zhong, D. Cao, and J.  ...  Bioucas-Dias 3352 Hyperspectral Data Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition .......................................................  ... 
doi:10.1109/tgrs.2020.2986115 fatcat:uisusulhrbcizgtvwir54g3wse

A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets

Rujie Yin, Tingran Gao, Yue M. Lu, Ingrid Daubechies
2017 SIAM Journal of Imaging Sciences  
Zhu, Low Dimensional Manifold Model for Image Processing, Tech. Rep., CAM report 16-04, UCLA, Los Angeles, CA, 2016].  ...  framework; a generalization of this framework to unions of local embeddings can provide a natural setting for interpreting BM3D, one of the state-of-the-art image denoising algorithms.  ...  We thank the authors of LDMM [45] for providing us with their code.  ... 
doi:10.1137/16m1091447 fatcat:5sig6hymwfbmrdvag3yoj473gi

Foveated Nonlocal Self-Similarity

Alessandro Foi, Giacomo Boracchi
2016 International Journal of Computer Vision  
We consider the image denoising problem as a simple means of assessing the effectiveness of descriptive models for natural images and we show that, in nonlocal image filtering, the foveated self-similarity  ...  To facilitate the use of foveation in nonlocal imaging algorithms, we develop a general framework for designing foveation operators for patches by means of spatially variant blur.  ...  Patch-based methods enforcing nonlocal self-similarity have been developed for video (Dabov et al., 2007a; Maggioni et al., 2012) , surface (Dong et al., 2008) , and point-cloud (Rosman et al., 2013  ... 
doi:10.1007/s11263-016-0898-1 fatcat:5d5zizsh7rfchmg4akryrtnj6e

Graph-Based Depth Denoising Dequantization for Point Cloud Enhancement [article]

Xue Zhang, Gene Cheung, Jiahao Pang, Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan
2021 arXiv   pre-print
Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes, in two established point cloud quality metrics.  ...  To improve quality, previous works denoise a point cloud a posteriori after projecting the imperfect depth data onto 3D space.  ...  This results in a noisy synthesized PC, and previous works focus on denoising PCs using a variety of methods: low-rank prior, low-dimensional manifold model (LDMM), surface smoothness priors expressed  ... 
arXiv:2111.04946v1 fatcat:fsxmrpoy6nelhpqngnzzxjxnye

A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

Chunhua Hu, Zhou Pan, Pingping Li
2019 Remote Sensing  
These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy  ...  When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise.  ...  Acknowledgments: We acknowledge the Advanced Analysis and Testing Center of Nanjing Forestry University for their assistance with data collection and technical support.  ... 
doi:10.3390/rs11020198 fatcat:qeee622atjetnjzihaez46jlrq

Manifold Modeling in Embedded Space: An Interpretable Alternative to Deep Image Prior

Tatsuya Yokota, Hidekata Hontani, Qibin Zhao, Andrzej Cichocki
2020 IEEE Transactions on Neural Networks and Learning Systems  
These results can also facilitate interpretation/characterization of DIP from the perspective of a "low-dimensional patch-manifold prior.".  ...  DIP empirically shows the effectiveness of the ConvNet structures for various image restoration applications. However, why the DIP works so well is still unknown.  ...  For example, it can be used for gray-scale image reconstruction if an image is regarded as a point cloud in 3-D space (i, j, X i j ).  ... 
doi:10.1109/tnnls.2020.3037923 pmid:33275587 fatcat:m6sliysmzfbqfar2yyqpaowyxe

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 2974-2985 Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images.  ...  Shah, D., +, JSTARS 2020 4200-4213 Evaluation of Convolution Operation Based on the Interpretation of Deep Learning on 3-D Point Cloud.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Table of contents

2020 IEEE Transactions on Geoscience and Remote Sensing  
Vegetation and Land Surface About the Cover: The cover shows multistatic ground penetrating radar (GPR) 3-D image formation for three buried PVC pipes.  ...  Alkhalifah 5836 Miscellaneous Applications Lattice-Constrained Stratified Sampling for Point Cloud Levels of Detail ............. K. L. Damkjer and H.  ... 
doi:10.1109/tgrs.2020.3003196 fatcat:zbz435hg2nfm5ejpetz3xg3yyu

Robust and Fast 3D Saliency Mapping for Industrial Modeling Applications

Gerasimos Arvanitis, Aris S. Lalos, Konstantinos Moustakas
2020 IEEE Transactions on Industrial Informatics  
and easier to analyze representation of a 3-D object. 3-D saliency mapping is, therefore, guiding the selection of feature locations and is adopted in a large number of low-level 3-D processing applications  ...  In this article, we propose a robust and fast method for creating 3-D saliency maps that accurately identifies sharp and small-scale geometric features in various industrial 3-D models.  ...  point clouds.  ... 
doi:10.1109/tii.2020.3003455 fatcat:33nztgodnfblrixvheno6vnrfy

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS May 2019 1489-1496 Hyperspectral Image Denoising via Subspace-Based Nonlocal Low-Rank and Sparse Factorization.  ...  ., +, JSTARS Oct. 2019 3784-3798 Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u
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