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Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning [article]

Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
<span title="2019-10-17">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only.  ...  This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds.  ...  To learn denoising of 3D point clouds, we need to extend from common noise that is clean in one part of the signal, to a total setting, where all parts of the signal are noisy.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.07615v2">arXiv:1904.07615v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nmlmkygiujbjdk3axikls6ej5a">fatcat:nmlmkygiujbjdk3axikls6ej5a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200824233045/https://arxiv.org/pdf/1904.07615v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/47/ee47891411f82065afc549c945749e302434ee85.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.07615v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

Pedro Hermosilla Casajus, Tobias Ritschel, Timo Ropinski
<span title="">2019</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/753trptklbb4nj6jquqadzwwdu" style="color: black;">2019 IEEE/CVF International Conference on Computer Vision (ICCV)</a> </i> &nbsp;
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only.  ...  This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds.  ...  To learn denoising of 3D point clouds, we need to extend from common noise that is clean in one part of the signal, to a total setting, where all parts of the signal are noisy.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccv.2019.00014">doi:10.1109/iccv.2019.00014</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iccv/Casajus0R19.html">dblp:conf/iccv/Casajus0R19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p7tdtyiyune3jcxe7kok5tspzu">fatcat:p7tdtyiyune3jcxe7kok5tspzu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107110956/https://discovery.ucl.ac.uk/id/eprint/10085047/10/Ritschel_Total%20Denoising.%20Unsupervised%20Learning%20of%203D%20Point%20Cloud%20Cleaning_AAM.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/36/cf/36cf1f135126354c102f62d95284c21e445e344c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccv.2019.00014"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Score-Based Point Cloud Denoising (Learning Implicit Gradient Fields for Point Cloud Denoising) [article]

Shitong Luo, Wei Hu
<span title="2022-03-12">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples p(x) convolved with some noise model n, leading to (p * n)(x) whose mode is the underlying clean  ...  To denoise a noisy point cloud, we propose to increase the log-likelihood of each point from p * n via gradient ascent – iteratively updating each point's position.  ...  the robustness of the neural denoiser. [11] proposed an unsupervised point cloud denoising framework-Total Denoising (TotalDn).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.10981v3">arXiv:2107.10981v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gysarl452jb6det6wr6g5a7tdi">fatcat:gysarl452jb6det6wr6g5a7tdi</a> </span>
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Differentiable Manifold Reconstruction for Point Cloud Denoising [article]

Shitong Luo, Wei Hu
<span title="2020-07-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
3D point clouds are often perturbed by noise due to the inherent limitation of acquisition equipments, which obstructs downstream tasks such as surface reconstruction, rendering and so on.  ...  By resampling on the reconstructed manifold, we obtain a denoised point cloud.  ...  Total Denoising [12] is the first unsupervised deep learning method for point cloud denoising.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.13551v1">arXiv:2007.13551v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zxlhgfjktrek7pn32ornvphl5i">fatcat:zxlhgfjktrek7pn32ornvphl5i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200927080926/https://arxiv.org/pdf/2007.13551v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7b/7d/7b7dc44922dd879004256cfb2cc9b8cc74a343be.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.13551v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Self-Supervised Deep Depth Denoising

Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras
<span title="2020-09-16">2020</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth.  ...  Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised endto-end manner using depth and  ...  The distance of each calculated plane of the ground-truth point cloud against the closest point from the denoised point clouds contribute a term to the final RMSE.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4032773">doi:10.5281/zenodo.4032773</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qic67tbrcrbwxdqh4tvoabfsuy">fatcat:qic67tbrcrbwxdqh4tvoabfsuy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200920002329/https://zenodo.org/record/4032773/files/18_CERTH_ICCV_2019.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/e6/62e69999781561642ecea27dc700c4299c6e389e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4032773"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Self-Supervised Deep Depth Denoising [article]

Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras
<span title="2019-09-04">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth.  ...  Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and  ...  The distance of each calculated plane of the ground-truth point cloud against the closest point from the denoised point clouds contribute a term to the final RMSE.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.01193v2">arXiv:1909.01193v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bgxuia4c5najfmiyejd5llyica">fatcat:bgxuia4c5najfmiyejd5llyica</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907171812/https://arxiv.org/pdf/1909.01193v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/71/4e/714e8ba4d9abd0526c87427350096b4836b9ecce.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.01193v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Non-Local Part-Aware Point Cloud Denoising [article]

Chao Huang, Ruihui Li, Xianzhi Li, Chi-Wing Fu
<span title="2020-03-14">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes.  ...  features over the entire point cloud.  ...  [6] designed TotalDenoising (TD) to denoise point clouds in an unsupervised manner.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.06631v1">arXiv:2003.06631v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o7gjmwbdxndj5btadq37ite6uu">fatcat:o7gjmwbdxndj5btadq37ite6uu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200320214809/https://arxiv.org/pdf/2003.06631v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.06631v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Dynamic Point Cloud Denoising via Gradient Fields [article]

Qianjiang Hu, Wei Hu
<span title="2022-04-19">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Although many efforts have been made for static point cloud denoising, dynamic point cloud denoising remains under-explored.  ...  The gradient field is the gradient of the log-probability function of the noisy point cloud, based on which we perform gradient ascent so as to converge each point to the underlying clean surface.  ...  Hence, 3D dynamic point cloud denoising is crucial to relevant 3D applications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.08755v1">arXiv:2204.08755v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oudh2yiyqzhkfmzrfli5d2zeci">fatcat:oudh2yiyqzhkfmzrfli5d2zeci</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220424223407/https://arxiv.org/pdf/2204.08755v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/52/4b/524b518d34a672f8f84ba7b139aff00b763464d0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.08755v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Learning Graph-Convolutional Representations for Point Cloud Denoising [article]

Francesca Pistilli, Giulia Fracastoro, Diego Valsesia, Enrico Magli
<span title="2020-07-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods.  ...  Point clouds are an increasingly relevant data type but they are often corrupted by noise.  ...  This material is based upon work supported by Google Cloud.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.02578v1">arXiv:2007.02578v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zv2dyootkvasfg6txymp6fn2z4">fatcat:zv2dyootkvasfg6txymp6fn2z4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710062232/https://arxiv.org/pdf/2007.02578v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.02578v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection [article]

Jihan Yang, Shaoshuai Shi, Zhe Wang, Hongsheng Li, Xiaojuan Qi
<span title="2021-08-15">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label denoising pipeline for unsupervised domain adaptation on 3D object detection.  ...  of the source domain.  ...  CONCLUSION We have presented ST3D++ -a holistic denoised self-training pipeline for unsupervised domain adaptive 3D object detection from point clouds.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.06682v1">arXiv:2108.06682v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nhpe3pvcufeabhtme5ou2fvxxi">fatcat:nhpe3pvcufeabhtme5ou2fvxxi</a> </span>
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A survey on deep learning-based Monte Carlo denoising

Yuchi Huo, Sung-eui Yoon
<span title="2021-03-29">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jnfwhcgai5dalfpeugj6pkswji" style="color: black;">Computational Visual Media</a> </i> &nbsp;
Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning, by training neural networks to reconstruct denoised rendering results from sparse MC samples  ...  Many of these techniques show promising results in real-world applications, and this report aims to provide an assessment of these approaches for practitioners and researchers.  ...  Because collecting 3D descriptors takes about half of the total rendering time, using light probes to collect 3D descriptors and minimizing the size of 3D descriptors dramatically reduce the overall computation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41095-021-0209-9">doi:10.1007/s41095-021-0209-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pki3mbbpw5fjxf6oaphcnlto4a">fatcat:pki3mbbpw5fjxf6oaphcnlto4a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718105640/https://link.springer.com/content/pdf/10.1007/s41095-021-0209-9.pdf?error=cookies_not_supported&amp;code=939ff0fa-fa42-469a-9d8b-4fbf791bf918" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/10/3110e306e2e890c89bba213cc2538ccd3dbab44b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41095-021-0209-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

DNF-Net: a Deep Normal Filtering Network for Mesh Denoising

Xianzhi Li, Ruihui Li, Lei Zhu, Chi-Wing Fu, Pheng-Ann Heng
<span title="2020-06-11">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hjrujdrg7zaghbdsp5pdzq7cmm" style="color: black;">IEEE Transactions on Visualization and Computer Graphics</a> </i> &nbsp;
Overall, DNF-Net is an end-to-end network that takes patches of facet normals as inputs and directly outputs the corresponding denoised facet normals of the patches.  ...  Besides the overall network architecture, our contributions include a novel multi-scale feature embedding unit, a residual learning strategy to remove noise, and a deeply-supervised joint loss function  ...  Subsequently, more network architectures were designed for handling various tasks on 3D point clouds, including object recognition [30] , [31] , [32] , unsupervised feature learning [33] , [34] ,  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tvcg.2020.3001681">doi:10.1109/tvcg.2020.3001681</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32746260">pmid:32746260</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w3y63aywnnh7jix7iu3kfhcdhe">fatcat:w3y63aywnnh7jix7iu3kfhcdhe</a> </span>
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DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs [chapter]

Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu
<span title="">2018</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The rendering equation is exploited in our network in an unsupervised manner. In detail, we impose an unsupervised loss based on the light transport to extract the high-frequency geometry.  ...  Thanks to the well decoupling of the low and high frequency information in the cascaded network, we achieve superior performance over the state-of-the-art techniques.  ...  The whole denoising net adopts the residual learning strategy to extract the latent clean image from noisy observation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-01249-6_10">doi:10.1007/978-3-030-01249-6_10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ru4ijo7novhj7gfkep5r326s4a">fatcat:ru4ijo7novhj7gfkep5r326s4a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180922012643/http://openaccess.thecvf.com:80/content_ECCV_2018/papers/Shi_Yan_DDRNet_Depth_Map_ECCV_2018_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fd/10/fd10db2f4ce20be80d460a1edbea56a4450b767f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-01249-6_10"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising [article]

Siheng Chen and Yonina C. Eldar and Lingxiao Zhao
<span title="2020-06-01">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We train the graph unrolling networks through unsupervised learning, where the input noisy graph signals are used to supervise the networks.  ...  By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input noisy graph signals, instead of manually choosing signal priors.  ...  Chen have attained remarkable success in social network analysis [8] , 3D point cloud processing [9] , quantum chemistry [10] and computer vision [11] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.01301v1">arXiv:2006.01301v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eggvqz7uerdbtpbxkh52s2u3bi">fatcat:eggvqz7uerdbtpbxkh52s2u3bi</a> </span>
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Optical flow estimation and denoising of video images based on deep learning models

Ang Li, Baoyu Zheng, Lei Li, Chen Zhang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
) under Grant 18kjd510004, in part by the Jiangsu province education information research project (New interactive education cloud platform design based on the Internet of things, and research on the teaching  ...  are effective, and it is suitable for moving image analysis, target tracking and 3D reconstruction Such research has certain theoretical significance and practical application value.  ...  Since the total length of all equipotential lines is less than the number of video pixels N, the complexity of this operation is not greater than O(N).  ... 
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