{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T02:19:01Z","timestamp":1784945941932,"version":"3.55.0"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200618","type":"print"},{"value":"9783031200625","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20062-5_14","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T10:31:55Z","timestamp":1668076315000},"page":"229-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Exploring the\u00a0Devil in\u00a0Graph Spectral Domain for\u00a03D Point Cloud Attacks"],"prefix":"10.1007","author":[{"given":"Qianjiang","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daizong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Atzmon, M., Maron, H., Lipman, Y.: Point convolutional neural networks by extension operators. arXiv preprint arXiv:1803.10091 (2018)","DOI":"10.1145\/3197517.3201301"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357 (2017)","DOI":"10.1109\/SP.2017.49"},{"issue":"3","key":"14_CR3","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TSP.2017.2771730","volume":"66","author":"S Chen","year":"2017","unstructured":"Chen, S., Tian, D., Feng, C., Vetro, A., Kova\u010devi\u0107, J.: Fast resampling of three-dimensional point clouds via graphs. IEEE Trans. Sig. Process. 66(3), 666\u2013681 (2017)","journal-title":"IEEE Trans. Sig. Process."},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907\u20131915 (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"14_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-3-030-58565-5_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Choi","year":"2020","unstructured":"Choi, J., Han, B.: Task-aware quantization network for JPEG image compression. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 309\u2013324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_19"},{"key":"14_CR6","unstructured":"Chung, F.R., Graham, F.C.: Spectral Graph Theory, vol. 92. American Mathematical Society (1997)"},{"key":"14_CR7","unstructured":"Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. arXiv preprint arXiv:1205.2653 (2012)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Duan, Y., Zheng, Y., Lu, J., Zhou, J., Tian, Q.: Structural relational reasoning of point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 949\u2013958 (2019)","DOI":"10.1109\/CVPR.2019.00104"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 605\u2013613 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Gao, X., Hu, W., Qi, G.J.: GraphTER: unsupervised learning of graph transformation equivariant representations via auto-encoding node-wise transformations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7163\u20137172 (2020)","DOI":"10.1109\/CVPR42600.2020.00719"},{"key":"14_CR12","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., Van Der Maaten, L.: 3D semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9224\u20139232 (2018)","DOI":"10.1109\/CVPR.2018.00961"},{"key":"14_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-58610-2_15","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Hamdi","year":"2020","unstructured":"Hamdi, A., Rojas, S., Thabet, A., Ghanem, B.: AdvPC: transferable adversarial perturbations on 3D point clouds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 241\u2013257. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_15"},{"issue":"2","key":"14_CR15","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmonic Anal. 30(2), 129\u2013150 (2011)","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Hu, W., Cheung, G., Li, X., Au, O.: Depth map compression using multi-resolution graph-based transform for depth-image-based rendering. In: Proceedings of the IEEE International Conference on Image Processing, pp. 1297\u20131300 (2012)","DOI":"10.1109\/ICIP.2012.6467105"},{"issue":"11","key":"14_CR17","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1109\/LSP.2015.2446683","volume":"22","author":"W Hu","year":"2015","unstructured":"Hu, W., Cheung, G., Ortega, A.: Intra-prediction and generalized graph Fourier transform for image coding. IEEE Sig. Process. Lett. 22(11), 1913\u20131917 (2015)","journal-title":"IEEE Sig. Process. Lett."},{"issue":"1","key":"14_CR18","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1109\/TIP.2014.2378055","volume":"24","author":"W Hu","year":"2015","unstructured":"Hu, W., Cheung, G., Ortega, A., Au, O.C.: Multiresolution graph Fourier transform for compression of piecewise smooth images. IEEE Trans. Image Process. 24(1), 419\u2013433 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Hu, W., Pang, J., Liu, X., Tian, D., Lin, C.W., Vetro, A.: Graph signal processing for geometric data and beyond: theory and applications. IEEE Trans. Multimedia (2021)","DOI":"10.1109\/TMM.2021.3111440"},{"issue":"9","key":"14_CR20","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850\u2013863 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR22","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: Learning convolutional networks for content-weighted image compression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3214\u20133223 (2018)","DOI":"10.1109\/CVPR.2018.00339"},{"key":"14_CR24","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on x-transformed points. In: Advances in Neural Information Processing Systems (NIPS), vol. 31, pp. 820\u2013830 (2018)"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Liu, D., Hu, W.: Imperceptible transfer attack and defense on 3D point cloud classification. arXiv preprint arXiv:2111.10990 (2021)","DOI":"10.1109\/TPAMI.2022.3193449"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Liu, D., Yu, R., Su, H.: Extending adversarial attacks and defenses to deep 3D point cloud classifiers. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2279\u20132283 (2019)","DOI":"10.1109\/ICIP.2019.8803770"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., Pan, C.: DensePoint: learning densely contextual representation for efficient point cloud processing. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 5239\u20135248 (2019)","DOI":"10.1109\/ICCV.2019.00534"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8895\u20138904 (2019)","DOI":"10.1109\/CVPR.2019.00910"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Ma, C., Meng, W., Wu, B., Xu, S., Zhang, X.: Efficient joint gradient based attack against SOR defense for 3D point cloud classification. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1819\u20131827 (2020)","DOI":"10.1145\/3394171.3413875"},{"key":"14_CR30","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"14_CR33","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013660 (2017)"},{"key":"14_CR34","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (NIPS) (2017)"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Ramasinghe, S., Khan, S., Barnes, N., Gould, S.: Spectral-GANs for high-resolution 3D point-cloud generation. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8169\u20138176. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341265"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Rosman, G., Dubrovina, A., Kimmel, R.: Patch-collaborative spectral point-cloud denoising. In: Computer Graphics Forum, vol. 32, pp. 1\u201312. Wiley (2013)","DOI":"10.1111\/cgf.12139"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Shen, G., Kim, W.S., Narang, S.K., Ortega, A., Lee, J., Wey, H.: Edge-adaptive transforms for efficient depth map coding. In: Proceedings of the Picture Coding Symposium, pp. 566\u2013569 (2010)","DOI":"10.1109\/PCS.2010.5702565"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Shen, Y., Feng, C., Yang, Y., Tian, D.: Mining point cloud local structures by kernel correlation and graph pooling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4548\u20134557 (2018)","DOI":"10.1109\/CVPR.2018.00478"},{"issue":"3","key":"14_CR39","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","unstructured":"Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Sig. Process. Mag. 30(3), 83\u201398 (2013)","journal-title":"IEEE Sig. Process. Mag."},{"key":"14_CR40","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3693\u20133702 (2017)","DOI":"10.1109\/CVPR.2017.11"},{"issue":"18","key":"14_CR41","doi-asserted-by":"publisher","first-page":"5097","DOI":"10.3390\/s20185097","volume":"20","author":"SP Singh","year":"2020","unstructured":"Singh, S.P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., Guly\u00e1s, B.: 3D deep learning on medical images: a review. Sensors 20(18), 5097 (2020)","journal-title":"Sensors"},{"key":"14_CR42","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 945\u2013953 (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"14_CR43","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"14_CR44","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"14_CR45","doi-asserted-by":"crossref","unstructured":"Tsai, T., Yang, K., Ho, T.Y., Jin, Y.: Robust adversarial objects against deep learning models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 954\u2013962 (2020)","DOI":"10.1609\/aaai.v34i01.5443"},{"key":"14_CR46","doi-asserted-by":"crossref","unstructured":"Tu, C.C., et al.: AutoZOOM: autoencoder-based zeroth order optimization method for attacking black-box neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 742\u2013749 (2019)","DOI":"10.1609\/aaai.v33i01.3301742"},{"key":"14_CR47","doi-asserted-by":"crossref","unstructured":"Wang, W., Yu, R., Huang, Q., Neumann, U.: SGPN: similarity group proposal network for 3D point cloud instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2569\u20132578 (2018)","DOI":"10.1109\/CVPR.2018.00272"},{"issue":"5","key":"14_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR49","unstructured":"Wen, Y., Lin, J., Chen, K., Chen, C.P., Jia, K.: Geometry-aware generation of adversarial point clouds. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) (2020)"},{"key":"14_CR50","doi-asserted-by":"crossref","unstructured":"Wicker, M., Kwiatkowska, M.: Robustness of 3D deep learning in an adversarial setting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11767\u201311775 (2019)","DOI":"10.1109\/CVPR.2019.01204"},{"key":"14_CR51","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912\u20131920 (2015)"},{"key":"14_CR52","unstructured":"Wu, Z., Duan, Y., Wang, H., Fan, Q., Guibas, L.J.: IF-defense: 3D adversarial point cloud defense via implicit function based restoration. arXiv preprint arXiv:2010.05272 (2020)"},{"key":"14_CR53","doi-asserted-by":"crossref","unstructured":"Xiang, C., Qi, C.R., Li, B.: Generating 3D adversarial point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9136\u20139144 (2019)","DOI":"10.1109\/CVPR.2019.00935"},{"key":"14_CR54","doi-asserted-by":"crossref","unstructured":"Xu, Q., Sun, X., Wu, C.Y., Wang, P., Neumann, U.: Grid-GCN for fast and scalable point cloud learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5661\u20135670 (2020)","DOI":"10.1109\/CVPR42600.2020.00570"},{"key":"14_CR55","unstructured":"Xu, Y., et al.: Predictive generalized graph Fourier transform for attribute compression of dynamic point clouds. arXiv preprint arXiv:1908.01970 (2019)"},{"key":"14_CR56","unstructured":"Yang, B., et al.: Learning object bounding boxes for 3D instance segmentation on point clouds. arXiv preprint arXiv:1906.01140 (2019)"},{"key":"14_CR57","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Modeling point clouds with self-attention and Gumbel subset sampling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3323\u20133332 (2019)","DOI":"10.1109\/CVPR.2019.00344"},{"key":"14_CR58","doi-asserted-by":"crossref","unstructured":"Yu, T., Meng, J., Yuan, J.: Multi-view harmonized bilinear network for 3D object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 186\u2013194 (2018)","DOI":"10.1109\/CVPR.2018.00027"},{"key":"14_CR59","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Advances in Neural Information Processing Systems (NIPS), vol. 30 (2017)"},{"key":"14_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, C., Florencio, D., Loop, C.: Point cloud attribute compression with graph transform. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2066\u20132070 (2014)","DOI":"10.1109\/ICIP.2014.7025414"},{"key":"14_CR61","unstructured":"Zhang, Q., Yang, J., Fang, R., Ni, B., Liu, J., Tian, Q.: Adversarial attack and defense on point sets. arXiv preprint arXiv:1902.10899 (2019)"},{"key":"14_CR62","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1109\/TIP.2020.3042088","volume":"30","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Cui, S., Ding, Z.: Hypergraph spectral analysis and processing in 3D point cloud. IEEE Trans. Image Process. 30, 1193\u20131206 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liang, G., Salem, T., Jacobs, N.: Defense-PointNet: protecting PointNet against adversarial attacks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5654\u20135660 (2019)","DOI":"10.1109\/BigData47090.2019.9006307"},{"key":"14_CR64","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wu, Y., Chen, C., Lim, A.: On isometry robustness of deep 3D point cloud models under adversarial attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1201\u20131210 (2020)","DOI":"10.1109\/CVPR42600.2020.00128"},{"key":"14_CR65","doi-asserted-by":"crossref","unstructured":"Zheng, T., Chen, C., Yuan, J., Li, B., Ren, K.: PointCloud saliency maps. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1598\u20131606 (2019)","DOI":"10.1109\/ICCV.2019.00168"},{"key":"14_CR66","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: LG-GAN: label guided adversarial network for flexible targeted attack of point cloud based deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10356\u201310365 (2020)","DOI":"10.1109\/CVPR42600.2020.01037"},{"key":"14_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., Yu, N.: DUP-Net: denoiser and Upsampler network for 3D adversarial point clouds defense. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1961\u20131970 (2019)","DOI":"10.1109\/ICCV.2019.00205"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20062-5_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:11:13Z","timestamp":1668125473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20062-5_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200618","9783031200625"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20062-5_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}