{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:13:32Z","timestamp":1743027212906,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030926588"},{"type":"electronic","value":"9783030926595"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-92659-5_44","type":"book-chapter","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T07:09:18Z","timestamp":1642057758000},"page":"681-696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantifying Point Cloud Realism Through Adversarially Learned Latent Representations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0037-8460","authenticated-orcid":false,"given":"Larissa T.","family":"Triess","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7950-9915","authenticated-orcid":false,"given":"David","family":"Peter","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0735-8713","authenticated-orcid":false,"given":"Stefan A.","family":"Baur","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6190-7202","authenticated-orcid":false,"given":"J. Marius","family":"Z\u00f6llner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"44_CR1","unstructured":"Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: Proceedings of the International Conference on Learning Representations (ICLR) Workshops (2018)"},{"key":"44_CR2","unstructured":"Arora, S., Risteski, A., Zhang, Y.: Do GANs learn the distribution? Some theory and empirics. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)"},{"key":"44_CR3","unstructured":"Beutel, A., Chen, J., Zhao, Z., Chi, E.H.: Data decisions and theoretical implications when adversarially learning fair representations. In: Workshop on Fairness, Accountability, and Transparency in Machine Learning (2017)"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Borji, A.: Pros and cons of GAN evaluation measures. In: Computer Vision and Image Understanding (CVIU), pp. 41\u201365 (2019)","DOI":"10.1016\/j.cviu.2018.10.009"},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Caccia, L., van Hoof, H., Courville, A., Pineau, J.: Deep generative modeling of LiDAR data. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 5034\u20135040 (2019)","DOI":"10.1109\/IROS40897.2019.8968535"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11618\u201311628 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77\u201385 (2017)","DOI":"10.1109\/CVPR.2017.16"},{"key":"44_CR8","unstructured":"Che, T., Li, Y., Jacob, A.P., Bengio, Y., Li, W.: Mode regularized generative adversarial networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)"},{"key":"44_CR9","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS) (2016)"},{"key":"44_CR10","series-title":"Methods in Molecular Biology","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-1-0716-0826-5_3","volume-title":"Artificial Neural Networks","author":"D Chicco","year":"2021","unstructured":"Chicco, D.: Siamese neural networks: an overview. In: Cartwright, H. (ed.) Artificial Neural Networks. Methods in Molecular Biology, vol. 2190, pp. 73\u201394. Springer, New York (2021). https:\/\/doi.org\/10.1007\/978-1-0716-0826-5_3"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"44_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-01261-8_28","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Dong","year":"2018","unstructured":"Dong, X., Shen, J.: Triplet loss in siamese network for object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 472\u2013488. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_28"},{"key":"44_CR13","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1\u201316 (2017)"},{"issue":"11","key":"44_CR14","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. (IJRR) 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res. (IJRR)"},{"key":"44_CR15","unstructured":"Goodfellow, I., et al..: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS) (2014)"},{"key":"44_CR16","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. (JMLR) 13, 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res. (JMLR)"},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Gurumurthy, S., Sarvadevabhatla, R.K., Babu, R.V.: DeLiGAN: generative adversarial networks for diverse and limited data. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4941\u20134949 (2017)","DOI":"10.1109\/CVPR.2017.525"},{"key":"44_CR18","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems (NIPS), pp. 6629\u20136640 (2017)"},{"key":"44_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-24261-3_7","volume-title":"Similarity-Based Pattern Recognition","author":"E Hoffer","year":"2015","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84\u201392. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24261-3_7"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: Stacked generative adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1866\u20131875 (2017)","DOI":"10.1109\/CVPR.2017.202"},{"key":"44_CR21","unstructured":"Im, D.J., Kim, C.D., Jiang, H., Memisevic, R.: Generating images with recurrent adversarial networks. arXiv.org (2016)"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105\u2013114 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"44_CR23","volume-title":"Testing Statistical Hypotheses","author":"EL Lehmann","year":"2006","unstructured":"Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses. Springer, Heidelberg (2006)"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Lin, Z., Khetan, A., Fanti, G., Oh, S.: PacGAN: the power of two samples in generative adversarial networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 324\u2013335 (2018)","DOI":"10.1109\/JSAIT.2020.2983071"},{"key":"44_CR25","unstructured":"Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs created equal? A large-scale study. In: Advances in Neural Information Processing Systems (NIPS), pp. 698\u2013707 (2018)"},{"key":"44_CR26","unstructured":"Mathieu, M.F., Zhao, J.J., Zhao, J., Ramesh, A., Sprechmann, P., LeCun, Y.: Disentangling factors of variation in deep representation using adversarial training. In: Advances in Neural Information Processing Systems (NIPS), pp. 5047\u20135055 (2016)"},{"key":"44_CR27","unstructured":"Olsson, C., Bhupatiraju, S., Brown, T., Odena, A., Goodfellow, I.: Skill rating for generative models. arXiv.org (2018)"},{"key":"44_CR28","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":"44_CR29","doi-asserted-by":"crossref","unstructured":"Raff, E., Sylvester, J.: Gradient reversal against discrimination: a fair neural network learning approach. In: Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 189\u2013198 (2018)","DOI":"10.1109\/DSAA.2018.00029"},{"key":"44_CR30","unstructured":"Richardson, E., Weiss, Y.: On GANs and GMMs. In: Advances in Neural Information Processing Systems (NIPS), pp. 5852\u20135863 (2018)"},{"key":"44_CR31","unstructured":"Salimans, T., et al.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems (NIPS), pp. 2234\u20132242 (2016)"},{"key":"44_CR32","unstructured":"Sallab, A.E., Sobh, I., Zahran, M., Essam, N.: LiDAR sensor modeling and data augmentation with GANs for autonomous driving. In: Proceedings of the International Conference on Machine learning (ICML) Workshops (2019)"},{"key":"44_CR33","unstructured":"Santurkar, S., Schmidt, L., Madry, A.: A classification-based study of covariate shift in GAN distributions. In: Proceedings of the International Conference on Machine learning (ICML), pp. 4480\u20134489 (2018)"},{"key":"44_CR34","unstructured":"Scale AI: PandaSet (2020). https:\/\/pandaset.org"},{"key":"44_CR35","doi-asserted-by":"crossref","unstructured":"Shu, D., Park, S.W., Kwon, J.: 3D point cloud generative adversarial network based on tree structured graph convolutions. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3858\u20133867 (2019)","DOI":"10.1109\/ICCV.2019.00396"},{"key":"44_CR36","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3389\/frobt.2018.00066","volume":"5","author":"L Sixt","year":"2018","unstructured":"Sixt, L., Wild, B., Landgraf, T.: RenderGAN: generating realistic labeled data. Front. Robot. AI 5, 6 (2018)","journal-title":"Front. Robot. AI"},{"key":"44_CR37","unstructured":"Srivastava, A., Valkov, L., Russell, C., Gutmann, M.U., Sutton, C.: VEEGAN: reducing mode collapse in GANs using implicit variational learning. In: Advances in Neural Information Processing Systems (NIPS) (2017)"},{"key":"44_CR38","unstructured":"Tolstikhin, I.O., Gelly, S., Bousquet, O., Simon-Gabriel, C.J., Sch\u00f6lkopf, B.: AdaGAN: boosting generative models. In: Advances in Neural Information Processing Systems (NIPS), pp. 5424\u20135433 (2017)"},{"key":"44_CR39","doi-asserted-by":"crossref","unstructured":"Triess, L.T., Peter, D., Rist, C.B., Enzweiler, M., Z\u00f6llner, J.M.: CNN-based synthesis of realistic high-resolution LiDAR data. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), pp. 1512\u20131519 (2019)","DOI":"10.1109\/IVS.2019.8813771"},{"key":"44_CR40","doi-asserted-by":"crossref","unstructured":"Xu, Q., et al.: An empirical study on evaluation metrics of generative adversarial networks. arXiv.org (2018)","DOI":"10.1109\/BigData.2018.8622525"},{"key":"44_CR41","unstructured":"Yang, J., Kannan, A., Batra, D., Parikh, D.: LR-GAN: layered recursive generative adversarial networks for image generation. In: Proceedings of the International Conf. on Learning Representations (ICLR) (2017)"},{"key":"44_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 5908\u20135916 (2017)","DOI":"10.1109\/ICCV.2017.629"},{"key":"44_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Decoupled learning for conditional adversarial networks. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 700\u2013708 (2018)","DOI":"10.1109\/WACV.2018.00082"},{"key":"44_CR44","unstructured":"Zhou, Z., et al.: Activation maximization generative adversarial nets. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92659-5_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T14:52:46Z","timestamp":1651762366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92659-5_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030926588","9783030926595"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92659-5_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"13 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bonn","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dagm-gcpr.de\/","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":"116","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":"46","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":"40% - 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":"2.95","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":"4.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}