Volume 15 Issue 2
Mar.  2022
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Article Contents
BI Yong, PAN Ming-qi, ZHANG Shuo, GAO Wei-nan. Overview of 3D point cloud super-resolution technology[J]. Chinese Optics, 2022, 15(2): 210-223. doi: 10.37188/CO.2021-0176
Citation: BI Yong, PAN Ming-qi, ZHANG Shuo, GAO Wei-nan. Overview of 3D point cloud super-resolution technology[J]. Chinese Optics, 2022, 15(2): 210-223. doi: 10.37188/CO.2021-0176

Overview of 3D point cloud super-resolution technology

doi: 10.37188/CO.2021-0176
Funds:  Supported by Special Project of Central Government Guiding Local Scienceand Technology Development in Beijing 2020(No. Z20111000430000)
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  • Corresponding author: wngao@mail.ipc.ac.cn
  • Received Date: 08 Oct 2021
  • Rev Recd Date: 28 Oct 2021
  • Accepted Date: 20 Dec 2021
  • Available Online: 24 Dec 2021
  • Publish Date: 21 Mar 2022
  • With the development of the computer vision technology, research on recording and modeling the real world accurately and efficiently has become a key issue. Due to the limitation of hardware, the resolution of a point cloud is usually low, which cannot meet the applications. Therefore, it is necessary to study the super-resolution technology of point clouds. In this paper, we sort out the significance, progress, and evaluation methods of 3D point cloud super-resolution technology, introduce the classical super-resolution algorithm and the super-resolution algorithm based on machine learning, summarize the characteristics of the current methods, and point out the main problems and challenges in current point cloud data super-resolution technology. Finally, the future direction in point cloud super-resolution technology is proposed.

     

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  • [1]
    李松泰. 三维激光扫描仪点云数据的应用研究[J]. 地矿测绘,2020,3(2):141-142.

    LI S T. Application of point cloud in 3D laser scanner[J]. Geological and Mineral Surveying and Mapping, 2020, 3(2): 141-142. (in Chinese)
    [2]
    杜瑞建, 葛宝臻, 陈雷. 多视高分辨率纹理图像与双目三维点云的映射方法[J]. 中国光学,2020,13(5):1055-1064. doi: 10.37188/CO.2020-0034

    DU R J, GE B ZH, CHEN L. Texture mapping of multi-view high-resolution images and binocular 3D point clouds[J]. Chinese Optics, 2020, 13(5): 1055-1064. (in Chinese) doi: 10.37188/CO.2020-0034
    [3]
    杜钦生, 李丹丹, 陈浩, 等. 结构光3D点云的PIN针针尖提取[J]. 液晶与显示,2021,36(9):1331-1340. doi: 10.37188/CJLCD.2020-0321

    DU Q SH, LI D D, CHEN H, et al. PIN tip extraction from 3D point cloud of structured light[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(9): 1331-1340. (in Chinese) doi: 10.37188/CJLCD.2020-0321
    [4]
    吴坤帅, 魏仲慧, 何昕, 等. 基于笔划三维深度特征的签名识别[J]. 液晶与显示,2019,34(10):1013-1020. doi: 10.3788/YJYXS20193410.1013

    WU K SH, WEI ZH H, HE X, et al. Signatures recognition based on strokes 3D depth feature[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(10): 1013-1020. (in Chinese) doi: 10.3788/YJYXS20193410.1013
    [5]
    谭红春, 耿英保, 杜炜. 一种高效的人脸三维点云超分辨率融合方法[J]. 光学技术,2016,42(6):501-505.

    TAN H CH, GENG Y B, DU W. An efficient method of face super-resolution fusion using 3D cloud points[J]. Optical Technique, 2016, 42(6): 501-505. (in Chinese)
    [6]
    张银, 任国全, 程子阳, 等. 三维激光雷达在无人车环境感知中的应用研究[J]. 激光与光电子学进展,2019,56(13):130001.

    ZHANG Y, REN G Q, CHENG Z Y, et al. Application research of there-dimensional LiDAR in unmanned vehicle environment perception[J]. Laser &Optoelectronics Progress, 2019, 56(13): 130001. (in Chinese)
    [7]
    王世峰, 戴祥, 徐宁, 等. 无人驾驶汽车环境感知技术综述[J]. 长春理工大学学报(自然科学版),2017,40(1):1-6.

    WANG SH F, DAI X, XU N, et al. Overview on environment perception technology for unmanned ground vehicle[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2017, 40(1): 1-6. (in Chinese)
    [8]
    杨必胜, 梁福逊, 黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报,2017,46(10):1509-1516. doi: 10.11947/j.AGCS.2017.20170351

    YANG B SH, LIANG F X, HUANG R G. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1509-1516. (in Chinese) doi: 10.11947/j.AGCS.2017.20170351
    [9]
    张绍阳, 侯旭阳, 崔华, 等. 利用激光散斑获取深度图[J]. 中国光学,2016,9(6):633-641.

    ZHANG SH Y, HOU X Y, CUI H, et al. Depth image acquisition using laser speckle[J]. Chinese Optics, 2016, 9(6): 633-641. (in Chinese)
    [10]
    卜禹铭, 杜小平, 曾朝阳, 等. 无扫描激光三维成像雷达研究进展及趋势分析[J]. 中国光学,2018,11(5):711-727. doi: 10.3788/co.20181105.0711

    BU Y M, DU X P, ZENG ZH Y, et al. Research progress and trend analysis of non-scanning laser 3D imaging radar[J]. Chinese Optics, 2018, 11(5): 711-727. (in Chinese) doi: 10.3788/co.20181105.0711
    [11]
    苏东, 张艳, 曲承志, 等. 基于彩色图像轮廓的深度图像修复方法[J]. 液晶与显示,2021,36(3):456-464. doi: 10.37188/CJLCD.2020-0222

    SU D, ZHANG Y, QU CH ZH, et al. Depth image restoration method based on color image contour[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(3): 456-464. (in Chinese) doi: 10.37188/CJLCD.2020-0222
    [12]
    FOIX S, ALENYA G, TORRAS C. Lock-in Time-of-Flight (ToF) cameras: a survey[J]. IEEE Sensors Journal, 2011, 11(9): 1917-1926. doi: 10.1109/JSEN.2010.2101060
    [13]
    SCHUON S, THEOBALT C, DAVIS J, et al. . High-quality scanning using time-of-flight depth superresolution[C]. Proceedings of 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2008: 1-7.
    [14]
    BALURE C S, KINI M R. Depth image super-resolution: a review and wavelet perspective[C]. Proceedings of International Conference on Computer Vision and Image Processing, Springer, 2017: 543-555.
    [15]
    肖宿, 韩国强, 沃焱. 数字图像超分辨率重建技术综述[J]. 计算机科学,2009,36(12):8-13,54. doi: 10.3969/j.issn.1002-137X.2009.12.003

    XIAO S, HAN G Q, WO Y. Survey of digital image super resolution reconstruction technology[J]. Computer Science, 2009, 36(12): 8-13,54. (in Chinese) doi: 10.3969/j.issn.1002-137X.2009.12.003
    [16]
    HARRIS J L. Diffraction and resolving power[J]. Journal of the Optical Society of America, 1964, 54(7): 931-936. doi: 10.1364/JOSA.54.000931
    [17]
    GOODMAN J W. Introduction to Fourier Optics[M]. San Francisco: McGraw-Hill, 1968.
    [18]
    谢海平, 谢凯利, 杨海涛. 图像超分辨率方法研究进展[J]. 计算机工程与应用,2020,56(19):34-41.

    XIE H P, XIE K L, YANG H T. Research progress of image super-resolution methods[J]. Computer Engineering and Applications, 2020, 56(19): 34-41. (in Chinese)
    [19]
    VAN OUWERKERK J D. Image super-resolution survey[J]. Image and Vision Computing, 2006, 24(10): 1039-1052. doi: 10.1016/j.imavis.2006.02.026
    [20]
    王浩, 张叶, 沈宏海, 等. 图像增强算法综述[J]. 中国光学,2017,10(4):438-448. doi: 10.3788/co.20171004.0438

    WANG H, ZHANG Y, SHEN H H, et al. Review of image enhancement algorithms[J]. Chinese Optics, 2017, 10(4): 438-448. (in Chinese) doi: 10.3788/co.20171004.0438
    [21]
    STARK H, OSKOUI P. High-resolution image recovery from image-plane arrays, using convex projections[J]. Journal of the Optical Society of America A, 1989, 6(11): 1715-1726. doi: 10.1364/JOSAA.6.001715
    [22]
    GEVREKCI M, PAKIN K. Depth map super resolution[C]. Proceedings of the 18th IEEE International Conference on Image Processing, IEEE, 2011: 3449-3452.
    [23]
    PATTI A J, ALTUNBASAK Y. Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants[J]. IEEE Transactions on Image Processing, 2001, 10(1): 179-186. doi: 10.1109/83.892456
    [24]
    TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]. Sixth International Conference on Computer Vision, IEEE, 1998: 839-846.
    [25]
    KOPF J, COHEN M F, LISCHINSKI D, et al. Joint bilateral upsampling[J]. ACM Transactions on Graphics, 2007, 26(3): 96-es. doi: 10.1145/1276377.1276497
    [26]
    涂义福, 张旭东, 张骏, 等. 基于边缘特征引导的深度图像超分率重建[J]. 计算机应用与软件,2017,34(2):220-225. doi: 10.3969/j.issn.1000-386x.2017.02.039

    TU Y F, ZHANG X D, ZHANG J, et al. Depth map super-resolution reconstruction based on the edge feature-guided[J]. Computer Applications and Software, 2017, 34(2): 220-225. (in Chinese) doi: 10.3969/j.issn.1000-386x.2017.02.039
    [27]
    YANG Q X, YANG R G, DAVIS J, et al.. Spatial-depth super resolution for range images[C]. Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007: 1-8.
    [28]
    CHAN D, BUISMAN H, THEOBALT C, et al.. A Noise-Aware Filter for Real-Time Depth Upsampling[C]. Multi-camera & Multi-modal Sensor Fusion Algorithms and Applications, Marseille, France: M2SFA2, 2008: inria-00326784.
    [29]
    HE K M, SUN J, TANG X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. doi: 10.1109/TPAMI.2012.213
    [30]
    FERSTL D, REINBACHER C, RANFTL R, et al. . Image guided depth upsampling using anisotropic total generalized variation[C]. 2013 IEEE International Conference on Computer Vision, IEEE, 2013: 993-1000.
    [31]
    邸维巍, 张旭东, 胡良梅, 等. 彩色图约束的二阶广义总变分深度图超分辨率重建[J]. 中国图象图形学报,2014,19(8):1162-1167. doi: 10.11834/jig.20140807

    DI W W, ZHANG X D, HU L M, et al. Depth image super-resolution based on second-order total generalized variation constrained by color image[J]. Journal of Image and Graphics, 2014, 19(8): 1162-1167. (in Chinese) doi: 10.11834/jig.20140807
    [32]
    王宇, 朴燕, 孙荣春. 结合同场景彩色图像的深度图超分辨率重建[J]. 光学学报,2017,37(8):0810002. doi: 10.3788/AOS201737.0810002

    WANG Y, PIAO Y, SUN R CH. Depth image super-resolution construction combined with high-resolution color image of the same scene[J]. Acta Optica Sinica, 2017, 37(8): 0810002. (in Chinese) doi: 10.3788/AOS201737.0810002
    [33]
    DIEBEL J, THRUN S. An application of markov random fields to range sensing[C]. Proceedings of the 18th Conference on Neural Information Processing Systems, ACM, 2005: 291-298.
    [34]
    陈金奇, 李榕. 一种基于改进MRF的深度图超分辨率重建[J]. 微处理机,2017,38(4):60-63,71. doi: 10.3969/j.issn.1002-2279.2017.04.015

    CHEN J Q, LI R. A depth map super-resolution reconstruction based on improved markov random field[J]. Microprocessors, 2017, 38(4): 60-63,71. (in Chinese) doi: 10.3969/j.issn.1002-2279.2017.04.015
    [35]
    PARK J, KIM H, TAI Y W, et al.. High quality depth map upsampling for 3D-TOF cameras[C]. 2011 International Conference on Computer Vision, IEEE, 2011: 1623-1630.
    [36]
    SCHARSTEIN D, PAL C. Learning conditional random fields for stereo[C]. 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007: 1-8.
    [37]
    DONG CH, LOY C C, HE K M, et al.. Learning a deep convolutional network for image super-resolution[C]. Proceedings of the 13th European Conference on Computer Vision, Springer, 2014: 184-199.
    [38]
    DONG CH, LOY C C, HE K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307. doi: 10.1109/TPAMI.2015.2439281
    [39]
    YU L Q, LI X ZH, FU C W, et al.. PU-Net: point cloud upsampling network[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 2790-2799.
    [40]
    CHARLES R Q, SU H, KAICHUN M, et al.. PointNet: deep learning on point Sets for 3D classification and segmentation[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 77-85.
    [41]
    QI C R, YI L, SU H, et al.. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, ACM, 2017: 5105-5114.
    [42]
    WANG Y F, WU SH H, HUANG H, et al.. Patch-based progressive 3D point set upsampling[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2019: 5951-5960.
    [43]
    GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139-144. doi: 10.1145/3422622
    [44]
    LI R H, LI X ZH, FU C W, et al.. PU-GAN: a point cloud upsampling adversarial network[C]. 2019 IEEE/CVF International Conference on Computer Vision, IEEE, 2019: 7202-7211.
    [45]
    YANG Y Q, FENG CH, SHEN Y R, et al.. FoldingNet: point cloud auto-encoder via deep grid deformation[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 206-215.
    [46]
    KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C]. 5th International Conference on Learning Representations, OpenReview. net, 2017.
    [47]
    QIAN G CH, ABUALSHOUR A, LI G H, et al.. PU-GCN: point cloud upsampling using graph convolutional networks[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2021: 11678-11687.
    [48]
    WU H, ZHANG J, HUANG K. Point Cloud Super Resolution with Adversarial Residual Graph Networks[J]. arXiv preprint, 2019: arXiv: 1908.02111.
    [49]
    YANG L B, WANG SH SH, MA S W, et al.. HiFaceGAN: face renovation via collaborative suppression and replenishment[C]. Proceedings of the 28th ACM International Conference on Multimedia, ACM, 2020: 1551-1560.
    [50]
    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. doi: 10.1016/j.robot.2020.103647
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