融合图注意力的多分辨率点云补全

潘李琳; 邵剑飞; 潘李琳; 邵剑飞

doi:10.7510/jgjs.issn.1001-3806.2023.05.019

[1]

缪永伟, 刘家宗, 陈佳慧, 等. 基于生成对抗网络的点云形状保结构补全[J]. 中国科学: 信息科学, 2020, 50(5): 675-691.MIAO Y W, LIU J Z, CHEN J H, et al. Structure-preserving shape completion of 3D point clouds with generative adversarial network[J]. SCIENTIA SINICA Informationis, 2020, 50(5): 675-691(in Chin-ese).

[2]

GUO Y L, WANG H Y, HU Q Y, et al. Deep learning for 3D point clouds: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(12): 4338-4364. doi: 10.1109/TPAMI.2020.3005434

[3]

PAULY M, MITRA N J, WALLNER J, et al. Discovering structural regularity in 3D geometry[J]. ACM Transactions on Graphics, 2008, 27(3): 287-297.

[4]

赵江洪, 孙铭悦, 王殷瑞, 等. 3维点云孔洞修复方法综述[J]. 测绘科学, 2021, 46(1): 114-123.ZHAO J H, SUN M Y, WANG Y R, et al. Review of three-dimensional point cloud completion methods[J]. Science of Surveying and Mapping, 2021, 46(1): 114-123 (in Chinese).

[5]

LI Y Y, DAI A, GUIBAS L, et al. Database-assisted object retrieval for real-time 3D reconstruction[J]. Computer Graphics Forum, 2015, 34(2): 435-446. doi: 10.1111/cgf.12573

[6]

代许松, 花向红, 田朋举, 等. 一种基于轴向偏离比的点云配准方法[J]. 测绘科学, 2021, 46(12): 98-105.DAI X S, HUA X H, TIAN P J, et al. A point cloud registration method based on axial deviation ratio[J]. Science of Surveying and Mapping, 2021, 46(12): 98-105 (in Chinese).

[7]

刘翠连, 陶于祥, 罗小波, 等. 混合卷积神经网络的高光谱图像分类方法[J]. 激光技术, 2022, 46(3): 355-361.LIU C L, TAO Y X, LUO X B, et al. Hyperspectral image classification based on hybrid convolutional neural network[J]. Laser Technology, 2022, 46(3): 355-361(in Chinese).

[8]

CHANG A X, FUNKHOUSER T, GUIBAS L, et al. ShapeNet: An information-rich 3D model repository[EB/OL]. (2015-12-09)[2022-06-20].https://arxiv.org/pdf/1512.03012.pdf.

[9]

QI C R, SU H, MO K, et al. Pointnet: Deep learning on point sets for 3D classification and segmentation[C]// IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 77-85.

[10]

YUAN W T, KHOT T, HELD D, et al. PCN: Point completion network[C]//2018 International Conference on 3D Vision. Verona, Italy: IEEE, 2018: 728-737.

[11]

HUANG Z, YU Y, XU J, et al. PF-Net: Point fractal network for 3D point cloud completion[C]//Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 2020: 7659-7667.

[12]

LEDIG C, THEIS L, HUSZAR F, et al. Photo realistic single image super-resolution using a generative adversarial network[C]// IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 4681-4690.

[13]

郑凯, 李建胜, 王俊强, 等. DCLS-GAN: 利用生成对抗网络的天绘一号卫星高原地区影像去云方法[J]. 测绘学报, 2021, 50(2): 248-259.ZHENG K, LI J Sh, WANG J Q, et al. DCLS-GAN: Cloud removal method for plateau area of TH-1 satellite image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2): 248-259 (in Chinese).

[14]

刘心溥, 马燕新, 许可, 等. 嵌入Transformer结构的多尺度点云补全[J]. 中国图象图形学报, 2022, 27(2): 538-549.LIU X P, MA Y X, XU K, et al. Multi-scale transformer based point cloud completion network[J]. Journal of Image and Graphics, 2022, 27(2): 538-549 (in Chinese).

[15]

QI C R, YI L, SU H, et al. Pointnet⁺⁺: Deep hierarchical feature learning on point sets in a metric space[C]// 31st Annual Conference on Neural Information Processing Systems. Cambridge, USA: Massachusetts Institute of Technology Press, 2017: 5099-5108.

[16]

CHEN C, FRAGONARA L Z, TSOURDOS A. GAPointNet: Graph attention based point neural network for exploiting local feature of point cloud[J]. Neurocomputing, 2021, 438(20): 122-132.

[17]

YANG Y, FENG C, SHEN Y, et al. FoldingNet: Point cloud auto-encoder via deep grid deformation[C]//Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 206-215.

[18]

缪永伟, 刘家宗, 孙瑜亮, 等. 基于类别-实例分割的室内点云场景修复补全[J]. 计算机学报, 2021, 44(11): 2189-2202.MIAO Y W, LIU J Z, SUN Y L, et al. Point cloud completion of indoor scenes based on category-instance segmentation[J]. Chinese Journal of Computers, 2021, 44(11): 2189-2202(in Chinese).

[19]

GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of wasserstein GANs[C]//31st International Conference on Neural Information Processing Systems. Washington, USA: IEEE, 2017: 5769-5779.

[20]

ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: International Conference on Machine Learning, 2017: 214-223.

[21]

FAN H Q, SU H, GUIBAS L. A point set generation network for 3D object reconstruction from a single image[C]//Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 2463-2471.

[22]

ACHLIOPTAS P, DIAMANTI O, MITLIAGKAS I, et al. Learning representations and generative models for 3D point clouds[C]//Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden: International Conference on Machine Learning, 2018: 40-49.

[23]

ZHAO Y H, BIRDAL T, DENG H W, et al. 3D point capsule networks[C]//Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2019: 1009-1018.