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In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3D ShapeNets: A deep representation for volumetric shapes,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR’15) , 2015, pp. 1912–1920
1920
Earlier work this paper cites.
H. W.Kuhn, “The hungarian method for the assignment problem,” Naval Research Logistics Quarterly , pp. 83–97, 1955
1955
Earlier work this paper cites.
R. Teghtsoonian, “On the exponents in Stevens’ law and the constant in Ekman’s law.” 1971
1971
Earlier work this paper cites.
M. Balsera, S. Stepaniants, S. Izrailev, Y. Oono, and K. Schulten, “Reconstructing potential energy functions from simulated force-induced unbinding processes,” Biophysical Journal , vol. 73, no. 3, pp. 1281–1287, 1997
1997
Earlier work this paper cites.
J.-M. Geusebroek, R. Van Den Boomgaard, A. W. Smeulders, and A. Dev, “Color and scale: The spatial structure of color images,” in European Conf. Computer Vision (ECCV’00) . Springer, 2000, pp. 331–341
2000
Earlier work this paper cites.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in IEEE Asilomar Conf. Signals, Systems & Computers (ACSS’2003) , vol. 2, 2003, pp. 1398–1402
2003
Earlier work this paper cites.
D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on pattern analysis and machine intelligence , vol. 25, no. 5, pp. 564–577, 2003
2003
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Processing , vol. 15, no. 11, pp. 3440–3451, 2006
2006
Earlier work this paper cites.
R. B. Rusu and S. Cousins, “3D is here: Point cloud library (PCL),” in IEEE Int. Conf. Robotics and Automation (ICRA’11) , 2011, pp. 1–4
2011
Earlier work this paper cites.
C. Dore and M. Murphy, “Integration of historic building information modeling (hbim) and 3D GIS for recording and managing cultural heritage sites,” in Int. Conf. Virtual Systems and Multimedia (ICVSM’12) , 2012, pp. 369–376
2012
Earlier work this paper cites.
R. BT.500-13, “Methodology for the subjective assessment of the quality of television pictures,” Jan. 2012
2012
Earlier work this paper cites.
M. Huang, G. Cui, M. Melgosa, M. Sánchez-Marañón, C. Li, M. R. Luo, and H. Liu, “Power functions improving the performance of color-difference formulas,” Optics Express , vol. 23, no. 1, pp. 597–610, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
R. Singh, A. Chakraborty, and B. S. Manoj, “Graph Fourier transform based on directed Laplacian,” in Int. Conf. Signal Processing and Communications (SPCOM’16) , 2016, pp. 1–5
2016
Earlier work this paper cites.
E. Alexiou, “On subjective and objective quality evaluation of point cloud geometry,” in IEEE Int. Conf. Quality of Multimedia Experience (QoMEX’17) , 2017, pp. 1–3
2017
Earlier work this paper cites.
E. Alexiou and T. Ebrahimi, “On the performance of metrics to predict quality in point cloud representations,” in Applications of Digital Image Processing XL , vol. 10396, 2017, p. 103961H
2017
Earlier work this paper cites.
D. Tian, H. Ochimizu, C. Feng, R. Cohen, and A. Vetro, “Geometric distortion metrics for point cloud compression,” in IEEE Int. Conf. Image Processing (ICIP’17) , 2017, pp. 3460–3464
2017
Earlier work this paper cites.
S. Chen, D. Tian, C. Feng, A. Vetro, and J. Kovačević, “Fast resampling of three-dimensional point clouds via graphs,” IEEE Trans. Signal Processing , vol. 66, no. 3, pp. 666–681, 2017
2017
Earlier work this paper cites.
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3d point clouds,” in Int. conf. Machine Learning . PMLR, 2018, pp. 40–49
2018
Earlier work this paper cites.
E. M. Torlig, E. Alexiou, T. A. Fonseca, R. L. de Queiroz, and T. Ebrahimi, “A novel methodology for quality assessment of voxelized point clouds,” in Applications of Digital Image Processing XLI , vol. 10752. Int. Soc. Optics and Photonics, 2018, p. 107520I
2018
Cited alongside, same era.
E. Alexious, A. M. Pinheiro, C. Duarte, D. Matković, E. Dumić, L. A. da Silva Cruz, L. G. Dmitrović, M. V. Bernardo, M. Pereira, and T. Ebrahimi, “Point cloud subjective evaluation methodology based on reconstructed surfaces,” in Applications of Digital Image Processing XLI , vol. 10752, 2018, p. 107520H
2018
Cited alongside, same era.
E. Alexiou, T. Ebrahimi, M. V. Bernardo, M. Pereira, A. Pinheiro, L. A. D. S. Cruz, C. Duarte, L. G. Dmitrovic, E. Dumic, D. Matkovics et al. , “Point cloud subjective evaluation methodology based on 2d rendering,” in IEEE Int. Conf. Quality of Multimedia Experience (QoMEX’18) , 2018, pp. 1–6
2018
Cited alongside, same era.
Q. Yang, H. Chen, Z. Ma, Y. Xu, R. Tang, and J. Sun, “Predicting the perceptual quality of point cloud: A 3D-to-2D projection-based exploration,” IEEE Trans. Multimedia , 2020
2020
Later among the works it cites.
A. Javaheri, C. Brites, F. Pereira, and J. Ascenso, “Mahalanobis based point to distribution metric for point cloud geometry quality evaluation,” IEEE Signal Processing Letters , vol. 27, pp. 1350–1354, 2020
2020
Later among the works it cites.
R. Diniz, P. G. Freitas, and M. C. Farias, “Local luminance patterns for point cloud quality assessment,” in IEEE Int. Workshop on Multimedia Signal Processing (MMSPW’20) , 2020, pp. 1–6
2020
Later among the works it cites.
E. Alexiou and T. Ebrahimi, “Towards a point cloud structural similarity metric,” in IEEE Int. Conf. Multimedia & Expo Workshops (ICMEW’20) , 2020, pp. 1–6
2020
Later among the works it cites.
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2018
Cited alongside, same era.
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry, “A papier-mâché approach to learning 3d surface generation,” in Proc. IEEE conf. Computer Vision and Pattern Recognition (CVPR’18) , 2018, pp. 216–224
2018
Cited alongside, same era.
L. Yu, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng, “PU-Net: Point cloud upsampling network,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR’18) , 2018, pp. 2790–2799
2018
Cited alongside, same era.
L. Zhang and Z. Zhu, “Unsupervised feature learning for point cloud understanding by contrasting and clustering using graph convolutional neural networks,” in 2019 Int. Conf. 3D Vision (3DV’19) , 2019, pp. 395–404
2019
Cited alongside, same era.
S. Chen, C. Duan, Y. Yang, D. Li, C. Feng, and D. Tian, “Deep unsupervised learning of 3D point clouds via graph topology inference and filtering,” IEEE Trans. Image Processing , vol. 29, pp. 3183–3198, 2019
2019
Cited alongside, same era.
Q. Yang, Z. Ma, Y. Xu, L. Yang, W. Zhang, and J. Sun, “Modeling the screen content image quality via multiscale edge attention similarity,” IEEE Trans. Broadcasting , vol. 66, no. 2, pp. 310–321, 2019
2019
Cited alongside, same era.
H. Su, Z. Duanmu, W. Liu, Q. Liu, and Z. Wang, “Perceptual quality assessment of 3D point clouds,” in IEEE Int. Conf. on Image Processing (ICIP’19) , 2019, pp. 3182–3186
2019
Cited alongside, same era.
L. A. da Silva Cruz, E. Dumić, E. Alexiou, J. Prazeres, R. Duarte, M. Pereira, A. Pinheiro, and T. Ebrahimi, “Point cloud quality evaluation: Towards a definition for test conditions,” in IEEE Int. Conf. Quality of Multimedia Experience (QoMEX’19) , 2019, pp. 1–6
2019
Cited alongside, same era.
E. Alexiou, “Exploiting user interactivity in quality assessment of point cloud imaging,” in IEEE Int. Conf. Quality of Multimedia Experience (QoMEX’19) , 2019, pp. 1–6
2019
Cited alongside, same era.
I. Viola, S. Subramanyam, and P. Cesar, “A color-based objective quality metric for point cloud contents,” in Int. Conf. Quality of Multimedia Experience (QoMEX’20) , 2020, pp. 1–6
2020
Later among the works it cites.
I. Viola and P. Cesar, “A reduced reference metric for visual quality evaluation of point cloud contents,” IEEE Signal Processing Letters , vol. 27, pp. 1660–1664, 2020
2020
Later among the works it cites.
D. Urbach, Y. Ben-Shabat, and M. Lindenbaum, “DPDist: Comparing point clouds using deep point cloud distance,” in European Conference on Computer Vision . Springer, 2020, pp. 545–560
2020
Later among the works it cites.
A. Javaheri, C. Brites, F. Pereira, and J. Ascenso, “Improving psnr-based quality metrics performance for point cloud geometry,” in IEEE Int. Conf. Image Processing (ICIP’20) , 2020, pp. 3438–3442
2020
Later among the works it cites.
2020
Later among the works it cites.
G. Qian, A. Abualshour, G. Li, A. Thabet, and B. Ghanem, “Pu-gcn: Point cloud upsampling using graph convolutional networks,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’21) , 2021, pp. 11 683–11 692
2021
Closest in time.
J. Pang, D. Li, and D. Tian, “Tearingnet: Point cloud autoencoder to learn topology-friendly representations,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’21) , 2021, pp. 7453–7462
2021
Closest in time.
Y. Zhang, Q. Yang, and Y. Xu, “MS-GraphSIM: Inferring point cloud quality via multiscale graph similarity,” in Proc. ACM Int. Conf. Multimedia (ACMMM’21) , 2021, pp. 1230–1238
2021
Closest in time.
R. Diniz, P. G. Freitas, and M. C. Q. Farias, “Color and geometry texture descriptors for point-cloud quality assessment,” IEEE Signal Processing Letters , vol. 28, pp. 1150–1154, 2021
2021
Closest in time.
Y. Xu, Q. Yang, L. Yang, and J.-N. Hwang, “EPES: Point cloud quality modeling using elastic potential energy similarity,” IEEE Trans. Broadcasting , vol. 68, no. 1, pp. 33–42, 2021
2021
Closest in time.
Q. Liu, H. Yuan, R. Hamzaoui, H. Su, J. Hou, and H. Yang, “Reduced reference perceptual quality model with application to rate control for video-based point cloud compression,” IEEE Trans. Image Processing , vol. 30, pp. 6623–6636, 2021
2021
Closest in time.
T. Wu, L. Pan, J. Zhang, T. WANG, Z. Liu, and D. Lin, “Balanced chamfer distance as a comprehensive metric for point cloud completion,” in Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34. Curran Associates, Inc., 2021, pp. 29 088–29 100
2021
Closest in time.
Q. Yang, Y. Liu, S. Chen, Y. Xu, and J. Sun, “No-reference point cloud quality assessment via domain adaptation,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22) , June 2022, pp. 21 179–21 188
2022
Closest in time.
Y. Liu, Q. Yang, Y. Xu, and L. Yang, “Point cloud quality assessment: Dataset construction and learning-based no-reference metric,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , 2022
2022
Closest in time.
Q. Liu, H. Su, T. Chen, H. Yuan, and R. Hamzaoui, “No-reference bitstream-layer model for perceptual quality assessment of v-pcc encoded point clouds,” IEEE Trans. Multimedia , pp. 1–1, 2022
2022
Closest in time.
Q. Liu, H. Su, Z. Duanmu, W. Liu, and Z. Wang, “Perceptual quality assessment of colored 3D point clouds,” IEEE Trans. Visualization and Computer Graphics , pp. 1–1, 2022
2022
Closest in time.
Pytorch unofficial implementation of PU-Net and PU-GAN. [Online]. Available: https://github.com/UncleMEDM/PUGAN-pytorch
2022
Closest in time.