三维点云上采样方法研究综述

doi:10.11896/jsjkx.230900110

Abstract

Abstract: With the popularity of three-dimensional(3D) scanning devices such as depth cameras and laser radars,the methods of representing 3D data using point clouds are becoming increasingly popular.The analysis and processing of point cloud data are also arousing great interest in the field of computer visual research.In fact,the quality of the original point clouds directly obtained by sensors is influenced by many factors,such as the self-occlusion of objects themselves,mutual occlusion between objects,differences in scanning accuracy,reflectivity,transparency,as well as environmental limitations during the scanning process,hardware limitations of scanning equipment,inevitably leading to noise,hollow,sparse point clouds.Therefore,obtaining high-quality dense and complete point clouds is an urgent task to be solved.Among them,point cloud upsampling is an important point cloud processing task that aims to transform sparse,non-uniform,and noisy point clouds into dense,uniform,and noiseless point clouds,and the quality of its results affects the quality of various downstream tasks.Therefore,some researchers have further explored and proposed various point cloud upsampling methods from multiple perspectives,so as to improve computing efficiency and network performance,and solve various difficult issues in point cloud upsampling.In order to promote future research on the point cloud upsampling task,first of all,the background and importance of this critical task are introduced.After that,the existing point cloud upsampling methods are comprehensively classified and reviewed from different task type perspectives,including geometric point cloud upsampling(GPU),arbitrary point cloud upsampling(APU),multi-attribute point cloud upsampling(MAPU),multi-modal point cloud upsampling(MMPU),scene point cloud upsampling(ScenePU)and sequential point cloud upsampling(SequePU).Then,the performance of these point cloud upsampling networks is analyzed and compared in detail.Finally,the existing problems and challenges are further analyzed,and possible future research directions are explored,hoping to provide new ideas for further research on 3D point cloud upsampling task and its downstream tasks(such as surface reconstruction)in the future.

Key words: Three-dimensional point clouds, Upsampling methods, Deep neural networks, Self-supervised learning, Three-dimensional reconstruction

CLC Number:

TP181

HAN Bing, DENG Lixiang, ZHENG Yi, REN Shuang. Survey of 3D Point Clouds Upsampling Methods[J].Computer Science, 2024, 51(7): 167-196.

References

[1]KATO S,TOKUNAGA S,YUYA M,et al.Autoware on board:Enabling autonomous vehicles with embedded systems[C]//Proceedings of the IEEE International Conference on Cyber-Physical Systems(ICCPS).2018:287-296.
[2]FRANCOIS P,FRANCIS C,ROLAND S.A review of pointcloud registration algorithms for mobile robotics[J].Foundations and Trends in Robotics,2015,4(1):1-10.
[3]QI C R,HAO S,MO K,et al.Pointnet:Deep learning on point sets for 3d classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2017:77-85.
[4]QI C R,LI Y,HAO S,et al.Pointnet++:Deep hierarchical feature learning on point sets in a metric space[C]//Advances in Neural Information Processing Systems(NeurIPS).2017:5099-5108.
[5]WANG Y,SUN Y B,LIU Z W,et al.Dynamic graph cnn for learning on point clouds[J].ACM Transactions on Graphics,2019,38(5):146:1-12.
[6]GUO Y L,WANG H Y,HU Q Y,et al.Deep learning for 3d point clouds:A survey[J].IEEE Transactions on Pattern Ana-lysis and Machine Intelligence,2021,43(12):4338-4364.
[7]GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? The kitti vision benchmark suite[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2012:3354-3361.
[8]DAI A,CHANG A X,SAVVA M,et al.Scannet:Richly-annotated 3d reconstructions of indoor scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2017:2432-2443.
[9]ZHANG Y,ZHAO W H,SUN B,et al.Point cloud upsampling algorithm:A systematic review[J].Algorithms,2022,15(4):1-17.
[10]WANG H C,ZHANG C L,LI M L.Research on point cloud upsampling algorithms based on deep learning[J].Journal of Image and Signal Processing,2023,12(1):21-31.
[11]WANG Z H,CHEN J,HOI S C.Deep learning for image super-resolution:A survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,43(10):3365-3387.
[12]HUANG H,WU S H,GONG M L,et al.Edge-aware point set resampling[J].ACM Transactions on Graphics.2013,32(1):9:1-9:12.
[13]YU L Q,LI X Z,FU C W,et al.Pu-net:Point cloud upsampling network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2018:2790-2799.
[14]YU L Q,LI X Z,FU C W,et al.Ec-net:An edge-aware point set consolidation network[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:1-17.
[15]WANG Y F,WU S H,HUANG H,et al.Patch-based progressive 3d point set upsampling[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2019:5958-5967.
[16]ZHANG W T,JIANG H L,YANG Z S H,et al.Data-driven upsampling of point clouds[J].Computer-Aided Design,2019,112:1-13.
[17]ZHOU K Y,DONG M,ARSLANTURK S Z.“Zero-shot” point cloud upsampling[J].arXiv:2106.13765,2022.
[18]ZENG G,LI H S,WANG X C,et al.Point cloud up-sampling network with multi-level spatial local feature aggregation[J].Computers and Electrical Engineering,2021,94:1-10.
[19]LU J,LI Z,CHENG H Z,et al.Denseconv for 3d point set upsampling[J].Electronics Letters,2021,57(10):397-399.
[20]WANG G Y,XU G,WU Q,et al.Two-stage point cloud super resolution with local interpolation and readjustment via outer-product neural network[J].Journal of Systems Science & Complexity,2021,34:68-82.
[21]DING D D,QIU C,LIU F C,et al.Point cloud upsampling via perturbation learning[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,31(12):4661-4672.
[22]QIAN G C,ABUALSHOUR A,LI G H,et al.Pu-gcn:Point cloud upsampling using graph convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2021:11683-11692.
[23]LI G H,MüLLER M,THABET A,et al.Deepgcns:Can gcns go as deep as cnns[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2019:9267-9276.
[24]BALLESTER P,ARAUJO R M.On the performance ofgooglenet and alexnet applied to sketches[C]//Proceedings of the AAAI Conference on Artificial Intelligence(AAAI).2016:1124-1128.
[25]MA H R,WANG J M,SUN Y K,et al.Pu-lngcn:Multi-scale design of point cloud upsampling using graph convolutional networks[C]//Proceedings of the International Conference on Computing and Artificial Intelligence(ICCAI).2022:711-718.
[26]GU F,ZHANG C L,WANG H Y,et al.Pu-wgcn:Point cloud upsampling using weighted graph convolutional networks[J].Remote Sensing,2022,14:1-20.
[27]AKHTAR ANIQUE,LI ZHU,AUWERA G V,et al.Pu-dense:Sparse tensor-based point cloud geometry upsampling[J].IEEE Transactions on Image Processing,2022,31:4133-4148.
[28]RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//Proceedings of the Medical Image Computing and Computer-Assisted Intervention(MICCAI).2015:234-241.
[29]MAO A H,DU Z H,HOU J H,et al.Pu-flow:A point cloud upsampling network with normalizing flows[J].IEEE Transactions on Visualization and Computer Graphics,2023,29(12):4964-4977.
[30]LONG C,ZHANG W X,LI R H,et al.Pc²-pu:Patch correlation and point correlation for effective point cloud upsampling[C]//Proceedings of the ACM International Conference on Multimedia(ACMMM).2022:2191-2201.
[31]LI Q,DAI T,XIA S T.A comparative study of feature expansion unit for 3d point cloud upsampling[J].arXiv:2205.09594,2022.
[32]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems(NeurIPS).2017:6000-6010.
[33]CORDONNIER J B,LOUKAS A,JAGGI M.On the relationship between self-attention and convolutional layers[C]//Proceedings of the International Conference on Learning Representations(ICLR).2020.
[34]HAN K,WANG Y H,CHEN H T,et al.A survey on vision transformer[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2023,45(1):87-110.
[35]LI R H,LI X Z,HENG P A,et al.Point cloud upsampling via disentangled refinement[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition(CVPR).2021:344-353.
[36]YEH C,LIN W C.Enhanced point cloud upsampling usingmulti-branch network and attention fusion[C]//Proceedings of the International Conference on Computer System,Information Technology,and Electrical Engineering(COSITE).2021:51-56.
[37]HAN B,ZHANG X Y,REN S.Pu-gacnet:Graph attention convolution network for point cloud upsampling[J].Image and Vision Computing,2022,118:1-13.
[38]WU M,JIAO H L,NAN J X.Sparse 3d point cloud parallel multi-scale feature extraction and dense reconstruction with multi-headed attentional upsampling[J].Electronics,2022,11(19):3157.
[39]LI Z Z,LI G,LI T H,et al.Semantic point cloud upsampling[J].IEEE Transactions on Multimedia,2022,18(9):1-11.
[40]ZHAO H S,JIANG L,JIA J Y,et al.Point transformer[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2021:16259-16268.
[41]QIU S,ANWAR S,BARNES N.Pu-transformer:Point cloudupsampling transformer[C]//Proceedings of the Asian Confe-rence on Computer Vision(ACCV).2022:2475-2493.
[42]WANG Y R,WANG S Y,SUN L H.Point cloud upsampling via a coarse-to-fine network[C]//Proceedings of the International Conference on Multimedia Modeling(MMM).2022:467-478.
[43]DU H,YAN X J,WANG J J,et al.Point cloud upsampling via cascaded refinement network[C]//Proceedings of the Asian Conference on Computer Vision(ACCV).2022:586-601.
[44]LEE H,LIM S.Pu-mfa:Point cloud up-sampling via multi-scale features attention[J].Sensors,2022,22(23):9308.
[45]ZHAO T M,LI L F,TIAN T,et al.Apunet:Attention-guided upsampling network for sparse and non-uniform point cloud[J].Pattern Recognition,2023,143:109796.
[46]CAI P P,WU Z Y,WU X Y,et al.Parametric surface constrai-ned upsampler network for point cloud[C]//Proceedings of the AAAI Conference on Artificial Intelligence(AAAI).2023:250-258.
[47]KIM D,SHIN M,PAIK J.Pu-edgeformer:Edge transformer for dense prediction in point cloud upsampling[J].arXiv:2305.01148,2023.
[48]DENG L X,HAN B,REN S.Upu-dgtnet:Dynamic graph transformer network for unsupervised point cloud upsampling[C]//Proceedings of the IEEE International Conference on Computer and Communications(ICCC).2022:1395-1399.
[49]HAN B,DENG L X,ZHENG Y,et al.Upu-snet:Siamese net-work for unsupervised point cloud upsampling based on spatial-aware transformers[J].Computers & Graphics,2023,115:484-499.
[50]LI R H,LI X Z,FU C W,et al.Pu-gan:A point cloud upsampling adversarial network[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2019:7203-7212.
[51]YANG Y Q,FENG C,SHEN Y R,et al.Foldingnet:Point cloud auto-encoder via deep grid deformation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2018:206-215.
[52]LV W Q,WEN H J,CHEN H.Point cloud upsampling by ge-nerative adversarial network with skip-attention[C]//Procee-dings of the International Symposium on Computer Engineering and Intelligent Communications(ISCEIC).2021:186-190.
[53]LI W T,HASEGAWA K,LI L,et al.Deep learning-based point upsampling for edge enhancement of 3d-scanned data and its application to transparent visualization[J].Remote Sensing,2021,13:1-26.
[54]ZHAO W H,WEN W,LIU K,et al.An improved point cloud upsampling algorithm for x-ray diffraction on thermal coatings of aeroengine blades[J].Applied Science,2022,12:1-12.
[55]SHEN B,LI L,HU X R,et al.A point cloud upsampling adversarial network based on residual multi-scale off-set attention[J].Virtual Reality & Intelligent Hardware,2023,5(1):81-91.
[56]WU H K,ZHANG J G,HUANG K Q.Point cloud super resolution with adversarial residual graph networks[J].arXiv:1908.02111,2019.
[57]LI X M,OWN C M,WU K Z,et al.Cm-net:A point cloud upsampling network based on adversarial neural network[C]//Proceedings of the International Joint Conference on Neural Networks(IJCNN).2021:1-8.
[58]LIU H,YUAN H,HAMZAOUI R,et al.Pu-refiner:A geome-try refiner with adversarial learning for point cloud upsampling[C]//Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).2022:2270-2274.
[59]LIU H,YUAN H,HOU J H,et al.Pufa-gan:A frequency-aware generative adversarial network for 3d point cloud upsampling[J].IEEE Transactions on Image Processing,2022,31:7389-7402.
[60]LI Z Z,WANG W M,LEI N,et al.Weakly supervised point cloud upsampling via optimal transport[C]ICASSP.2022:2564-2568.
[61]LIU X H,HAN Z Z,WEN X,et al.L2g auto-encoder:Understanding point clouds by local-to-global reconstruction with hie-rarchical self-attention[C]//Proceedings of the ACM International Conference on Multimedia(ACMMM).2019:989-997.
[62]CHEN H L,LUO S T,GAO X,et al.Unsupervised learning of geometric sampling invariant representations for 3d point clouds[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops(ICCVW).2021:893-903.
[63]ZHAO Y F,HUI L,XIE J.Sspu-net:Self-supervised point cloud upsampling via differentiable rendering[C]//Proceedings of the ACM International Conference on Multimedia(ACMMM).2021:2214-2223.
[64]LIU X H,LIU X C,HAN Z Z,et al.Spu-net:Self-supervised point cloud upsampling by coarse-to-fine reconstruction with self-projection optimization[J].IEEE Transactions on Image Processing,2022,31:4213-4226.
[65]ZHANG C,SHI J,DENG X,et al.Upsampling autoencoder for self-supervised point cloud learning[J].arXiv:2203.10768,2022.
[66]ZHAO W B,LIU X M,ZHONG Z W,et al.Self-supervised arbitrary-scale point clouds upsampling via implicit neural representation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2022:1999-2007.
[67]ZHAO W B,LIU X M,ZHAI D M,et al.Self-supervised arbitrary-scale implicit point clouds upsampling[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2023,45(10):12394-12407.
[68]ZHANG P P,WANG X,MA L,et al.Progressive point cloud upsampling via differentiable rendering[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,31(12):4673-4685.
[69]BAI Y C,WANG X G,JR M H A,et al.Bims-pu:Bi-directional and multi-scale point cloud upsampling[J].IEEE Robotics and Automation Letters,2022,7(3):7447-7454.
[70]LIU Z S,WANG Z J,JIA Z.Arbitrary point cloud upsampling via dual back-projection network[J].arXiv:2307.08992,2023.
[71]QIAN Y,HOU J H,KWONG S,et al.Deep magnification-flexible upsampling over 3d point clouds[J].IEEE Transactions on Image Processing,2021,30:8354-8367.
[72]YE S Q,CHEN D D,HAN S F,et al.Meta-pu:An arbitrary-scale upsampling network for point cloud[J].IEEE Transactions on Visualization and Computer Graphics,2022,28(9):3206-3218.
[73]HE Y,TANG D H,ZHANG Y D,et al.Grad-pu:Arbitrary-scale point cloud upsampling via gradient descent with learned distance functions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2023:5354-5363.
[74]ZHONG F,BAI Z Y.Psr-gat:Arbitrary point cloud super-resolution using graph attention networks[J].Multimedia Tools and Applications,2023,83:26213-26232.
[75]DELL’EVA A,ORSINGHER M,BERTOZZI M.Arbitrarypoint cloud upsampling with spherical mixture of gaussians[C]//Proceedings of the International Conference on 3D Vision(3DV).2022:465-474.
[76]HEIMANN V,SPRUCK A,KAUP A.Frequency-selective geometry upsampling of point clouds[C]//Proceedings of the IEEE International Conference on Image Processing(ICIP).2022:1511-1515.
[77]FENG W Q,LI J,CAI H R,et al.Neural points:Pointcloud representation with neural fields[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2022:18633-18642.
[78]DINESH C,CHEUNG G,BAJIC＇ I V.3D point cloud super-resolution via graph total variation on surface normals[C]//Proceedings of the IEEE International Conference on Image Processing(ICIP).2019:4390-4394.
[79]QIAN Y,HOU J H,KWONG S,et al.Pugeo-net:A geometry-centric network for 3d point cloud upsampling[C]//Proceedings of the European Conference on Computer Vision(ECCV).2020:1-17.
[80]SHARMA R,SCHWANDT T,KUNERT C,et al.Point cloud upsampling and normal estimation using deep learning for robust surface reconstruction[C]//Proceedings of the International Joint Conference on Computer Vision,Imaging and Computer Graphics Theory and Applications(VISIGRAPP).2021:70-79.
[81]ARSHAD M S,BEKSI W J.A progressive conditional generative adversarial network for generating dense and colored 3d point clouds[C]//Proceedings of the International Conference on 3D Vision(3DV).2020:712-722.
[82]NAIK S,MUDENAGUDI U,TABIB R,et al.Featurenet:Upsampling of point cloud and it’s associated features[C]//Proceedings of the Special Interest Group for Computer Graphics(SIGGRAPH).2020.
[83]DINESH C,CHEUNG G,BAJIC＇ I V.Super-resolution of 3d color point clouds via fast graph total variation[C]//Procee-dings of the IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).2020.
[84]HEIMANN V,SPRUCK A,KAUP A.Frequency-selectivemesh-to-mesh resampling for color upsampling of point clouds[C]//Proceedings of the IEEE International Workshop on Multimedia Signal Processing(MMSP).2021.
[85]KIM B,HAN S,YI E,et al.3D point cloud upsampling and colorization using gan[C]//Proceedings of the International Confe-rence on Multi-disciplinary Trends in Artificial Intelligence(MIWAI).2021:1-17.
[86]BORGES T M,GARCIA D C,QUEIROZ R L.Fractional super-resolution of voxelized point clouds[J].IEEE Transactions on Image Processing,2022,31:1380-1390.
[87]GRAZIOSI D B,NAKAGAMI O,KUMA S,et al.An overview of ongoing point cloud compression standardization activities:Video-based(V-PCC)and geometry-based(GPCC)[J].APSIPA Transactions on Signal and Information Processing,2020,9(1):1-17.
[88]WANG L D,HAJIESMAILI M,CHAKARESKI J,et al.Cu-net:Real-time high-fidelity color upsampling for point clouds[J].arXiv:2209.06112,2022.
[89]HEIMANN V,SPRUCK A,KAUP A.Joint geometry and at-tribute upsampling of point clouds using frequency-selective models with overlapped support[J].arXiv:2301.11630,2023.
[90]NGUYEN N H T,PERRY S,BONE D,et al.Combination of images and point clouds in a generative adversarial network for upsampling crack point clouds[J].IEEE Access,2022,10:67198-67209.
[91]CHEN T,HSIAO C,CHENG W H,et al.Denser:Density-imbalance-eased representation for lidar-based whole scene upsampling[C]//Proceedings of the International Conference on Vi-sual Communications and Image Processing(VCIP).2021:1-5.
[92]CHEN T,HSIAO C,HUANG C C.Density-imbalance-eased lidar point cloud upsampling via feature consistency learning[J].IEEE Transactions on Intelligent Vehicles,2023,8(4):2875-2887.
[93]SAVKIN A,WANG Y D,WIRKERT S,et al.Lidar upsampling with sliced wasserstein distance[J].arXiv:2301.13558,2023.
[94]YAO Y K,FATHOLAHI S,CHEN Y P,et al.Indoor lidarpoint clouds upsampling for object detection enhancement[C]//Proceedings of the Joint Urban Remote Sensing Event(JURSE).2023.
[95]SHAN T X,WANG J K,CHEN F F,et al.Simulation-based lidar super-resolution for ground vehicles[J].Robotics and Autonomous Systems,2020,134:103647.
[96]ESKANDAR G,PALANISWAMY J,GUIRGUIS K,et al.Glpu:A geometric approach for lidar pointcloud upsampling[J].arXiv:2202.03901,2021.
[97]JUNG Y,SEO S W,KIM S W.Fast point clouds upsampling with uncertainty quantification for autonomous vehicles[C]//Proceedings of the IEEE International Conference on Robotics and Automation(ICRA).2022:7776-7782.
[98]MOYNIHAN M,PAGÉS R,SMOLIC A.Spatio-temporal up-sampling for free viewpoint video point clouds[C]//Proceedings of the International Joint Conference on Computer Vision,Imaging and Computer Graphics Theory and Applications(VISAPP).2019.
[99]WANG K,SHENG L,GU S H,et al.Sequential point cloud upsampling by exploiting multi-scale temporal dependency[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,31(12),4686-4696.
[100]WANG K,SHENG L,GU S H,et al.Vpu:A video-based point cloud upsampling framework[J].IEEE Transactions on Image Processing,2022,31:4062-4075.
[101]LI Z J,LI T Q,FARIMANI A B.Tpu-gan:Learning temporal coherence from dynamic point cloud sequences[C]//Proceedings of the International Conference on Learning Representations.2022:1-21.
[102]BERLINCIONI L,BERTINI M,BERRETTI S,et al.4Dsr-gcn:4D video point cloud upsampling using graph convolutional networks[J].arXiv:2306.01081,2023.
[103]ARMENI I,SENER O,ZAMIR A R,et al.3D semantic parsing of large-scale indoor spaces[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:1534-1543.
[104]UY M A,PHAM Q H,HUA B S,et al.Revisiting point cloud classification:A new benchmark dataset and classification model on real-world data[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2019:1588-1597.
[105]NOURI A,CHARRIER C,LÉZORAY O.Technical report:Greyc 3d colored mesh database[R].Normandie Université,2017.
[106]KRIVOKUC＇A M,CHOU P A,SAVILL P.8i voxelized surface light field(8ivslf)dataset[S].ISO/IEC JTC1/SC29 WG11(MPEG)input document m42914,Ljubljana,2018.
[107]PONS-MOLL G,ROMERO J,MAHMOOD N,et al.Dyna:A model of dynamic human shape in motion[J].ACM Transactions on Graphics,2015,34(4):120:1-120:14.
[108]RANJAN A,BOLKART T,SANYAL S,et al.Generating 3dfaces using convolutional mesh autoencoders[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:704-720.
[109]CHANG A X,FUNKHOUSER T,GUIBAS L,et al.Shapenet:An information-rich 3d model repository[J].arXiv:1512.03012,2015.
[110]CORSINI M,CIGNONI P,SCOPIGNO R.Efficient and flexible sampling with blue noise properties of triangular meshes[J].IEEE Transactions on Visualization and Computer Graphics,2012,18(6):914-924.
[111]FAN H Q,SU H,GUIBAS L.A point set generation network for 3d object reconstruction from a single image[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2017:605-613.
[112]LIU M H,SHENG L,YANG S,et al.Morphing and sampling network for dense point cloud completion[C]//Proceedings of the AAAI Conference on Artificial Intelligence(AAAI).2020:11597-11603.
[113]MATTHEW B,LEVINE J A,NONATO L G,et al.A benchmark for surface reconstruction[J].ACM Transactions on Graphics,2013,32:1-17.
[114]TIAN D,OCHIMIZU H,FENG C,et al.Geometric distortionmetrics for point cloud compression[C]//Proceedings IEEE International Conference on Image Processing(ICIP).2017:3460-3464.
[115]ALEXIOU E,EBRAHIMI T.Towards a point cloud structural similarity metric[C]//2020 IEEE International Conference on Multimedia and Expo Workshops.2020.
[116]XIANG T G,ZHANG C Y,SONG Y,YU J H,CAI W D.Walk in the cloud:Learning curves for point clouds shape analysis[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2021:915-924.
[117]WU Z R,SONG S R,KHOSLA A,et al.3d shapenets:A deep representation for volumetric shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2015:1912-1920.
[118]SON H,KIM Y M.Saum:Symmetry-aware upsampling module for consistent point cloud completion[C]//Proceedings of the Asian Conference on Computer Vision(ACCV).2020:1-17.
[119]ZHAO Y F,XIE J,QIAN J J,et al.Pui-net:A point cloud upsampling and inpainting network[C]//Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision(PRCV).2020:328-340.
[120]ZHOU H,CHEN K J,ZHANG W M,et al.Dup-net:Denoiser and upsampler network for 3d adversarial point clouds defense[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2019:1961-1970.
[121]LI J W,HUANG Y N,LEI Y Q.Self-supervised point cloudlearning in few-shot scenario by point up-sampling and mutual information neural estimation[C]//Proceedings of the International Conference on Computer and Communication Systems(ICCCS).2022:304-309.
[122]MANDIKAL P,BABU R V.Dense 3d point cloud reconstruction using a deep pyramid network[C]//Proceedings of the IEEE Winter Conference on Applications of Computer Vision(WACV).2019:1052-1060.
[123]REN S Y,HOU J H,CHEN X D,et al.Geoudf:Surface reconstruction from 3d point clouds via geometry-guided distance representation[J].arXiv:2211.16762,2023.
[124]CROITORU F A,HONDRU V,IONESCU R T,et al.Diffusion models in vision:A survey[J].arXiv:2209.04747,2022.
[125]LUO S T,HU W.Diffusion probabilistic models for 3d point cloud generation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2021:2837-2845.
[126]ZHOU L Q,DU Y L,WU J J.3D shape generation and completion through point-voxel diffusion[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2021:5826-5835.
[127]LYU Z Y,KONG Z F,XU X D,et al.A conditional point diffusion-refinement paradigm for 3d point cloud completion[C]//Proceedings of the International Conference on Learning Representations(ICLR).2021.
[128]WU L M,WANG D L,GONG C Y,et al.Fast point cloud ge-neration with straight flows[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2023:9445-9454.
[129]MELAS-KYRIAZI L,RUPPRECHT C,VEDALDI A.Pc²:Projection-conditioned point cloud diffusion for single-imag.e 3d reconstruction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2023:12923-12932.
[130]ZHANG K,ZHOU H,ZHANG J,et al.Ada3diff:Defendingagainst 3d adversarial point clouds via adaptive diffusion[J].ar-Xiv:2211.16247,2022.
[131]GUAN T R,MUTHUSELVAM A,HOOVER M,et al.Crossloc3d:Aerial-ground cross-source 3d place recognition[J].ar-Xiv:2303.17778,2023.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Survey of 3D Point Clouds Upsampling Methods

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	XU Xiaohua, ZHOU Zhangbing, HU Zhongxu, LIN Shixun, YU Zhenjie. Lightweight Deep Neural Network Models for Edge Intelligence:A Survey [J]. Computer Science, 2024, 51(7): 257-271.
[2]	LI Wenting, XIAO Rong, YANG Xiao. Improving Transferability of Adversarial Samples Through Laplacian Smoothing Gradient [J]. Computer Science, 2024, 51(6A): 230800025-6.
[3]	CHEN Wenzhong, CHEN Hongmei, ZHOU Lihua, FANG Yuan. Time-aware Pre-training Method for Sequence Recommendation [J]. Computer Science, 2024, 51(5): 45-53.
[4]	LI Xiang, FAN Zhiguang, LIN Nan, CAO Yangjie, LI Xuexiang. Self-supervised Learning for 3D Real-scenes Question Answering [J]. Computer Science, 2023, 50(9): 220-226.
[5]	ZENG Wu, MAO Guojun. Few-shot Learning Method Based on Multi-graph Feature Aggregation [J]. Computer Science, 2023, 50(6A): 220400029-10.
[6]	ZHU Lei, WANG Shanmin, LIU Qingshan. Self-supervised 3D Face Reconstruction Based on Detailed Face Mask [J]. Computer Science, 2023, 50(2): 214-220.
[7]	WANG Pengyu, TAI Wenxin, LIU Fang, ZHONG Ting, LUO Xucheng, ZHOU Fan. Self-supervised Flight Trajectory Prediction Based on Data Augmentation [J]. Computer Science, 2023, 50(2): 130-137.
[8]	REN Shuyao, SONG Jiangling, ZHANG Rui. Early Screening Method for Depression Based on EEG Signal [J]. Computer Science, 2023, 50(11A): 221100139-6.
[9]	DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[10]	QIAN Dong-wei, CUI Yang-guang, WEI Tong-quan. Secondary Modeling of Pollutant Concentration Prediction Based on Deep Neural Networks with Federal Learning [J]. Computer Science, 2022, 49(11A): 211200084-5.
[11]	MIAO Zhuang, WANG Ya-peng, LI Yang, WANG Jia-bao, ZHANG Rui, ZHAO Xin-xin. Robust Hash Learning Method Based on Dual-teacher Self-supervised Distillation [J]. Computer Science, 2022, 49(10): 159-168.
[12]	FAN Hong-jie, LI Xue-dong, YE Song-tao. Aided Disease Diagnosis Method for EMR Semantic Analysis [J]. Computer Science, 2022, 49(1): 153-158.
[13]	TIAN Song-wang, LIN Su-zhen, YANG Bo. Multi-band Image Self-supervised Fusion Method Based on Multi-discriminator [J]. Computer Science, 2021, 48(8): 185-190.
[14]	ZHOU Xin, LIU Shuo-di, PAN Wei, CHEN Yuan-yuan. Vehicle Color Recognition in Natural Traffic Scene [J]. Computer Science, 2021, 48(6A): 15-20.
[15]	XIAO Rui, JIANG Jia-qi, ZHANG Yun-chun. Study on Semantic Topology and Supervised Word Sense Disambiguation of Polysemous Words [J]. Computer Science, 2019, 46(11A): 13-18.