{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:37:22Z","timestamp":1742924242895,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930455"},{"type":"electronic","value":"9783030930462"}],"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\/978-3-030-93046-2_54","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T05:30:01Z","timestamp":1641015001000},"page":"638-649","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ARShape-Net: Single-View Image Oriented 3D Shape Reconstruction with an Adversarial Refiner"],"prefix":"10.1007","author":[{"given":"Hao","family":"Xu","sequence":"first","affiliation":[]},{"given":"Jing","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"54_CR1","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, pp. 1912\u20131920 (2015)"},{"key":"54_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46484-8_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"CB Choy","year":"2016","unstructured":"Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628\u2013644. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_38"},{"key":"54_CR3","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, W. T., Tenenbaum, J. B.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 82\u201390 (2016)"},{"key":"54_CR4","unstructured":"Zhang, X., Zhang, Z., Zhang, C., Tenenbaum, J.B., Freeman, W.T., Wu, J.: Learning to reconstruct shapes from unseen classes. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 2263\u20132274 (2018)"},{"key":"54_CR5","unstructured":"Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W.T., Tenenbaum, J.B.: MarrNet: 3D shape reconstruction via 2.5 d sketches. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 540\u2013550 (2017)"},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Xie, H., Yao, H., Sun, X., Zhou, S., Zhang, S.: Pix2vox: context-aware 3D reconstruction from single and multi-view images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2690\u20132698 (2019)","DOI":"10.1109\/ICCV.2019.00278"},{"key":"54_CR7","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011)"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Richter, S.R., Roth, S.: Discriminative shape from shading in uncalibrated illumination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1128\u20131136 (2015)","DOI":"10.1109\/CVPR.2015.7298716"},{"key":"54_CR9","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"54_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/978-3-030-01252-6_40","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Wu","year":"2018","unstructured":"Wu, J., Zhang, C., Zhang, X., Zhang, Z., Freeman, W.T., Tenenbaum, J.B.: Learning shape priors for single-view 3D completion and reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 673\u2013691. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_40"},{"issue":"1\u20133","key":"54_CR11","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0004-3702(81)90019-9","volume":"17","author":"AP Witkin","year":"1981","unstructured":"Witkin, A.P.: Recovering surface shape and orientation from texture. Artif. Intell. 17(1\u20133), 17\u201345 (1981)","journal-title":"Artif. Intell."},{"key":"54_CR12","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 922\u2013928 (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"54_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"54_CR14","doi-asserted-by":"crossref","unstructured":"Tulsiani, S., Zhou, T., Efros, A.A., Malik, J.: Multi-view supervision for single-view reconstruction via differentiable ray consistency. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2626\u20132634 (2017)","DOI":"10.1109\/CVPR.2017.30"},{"key":"54_CR15","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, pp. 605\u2013613 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"54_CR16","doi-asserted-by":"crossref","unstructured":"Dibra, E., Jain, H., Oztireli, C., Ziegler, R., Gross, M.: Human shape from silhouettes using generative HKS descriptors and cross-modal neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4826\u20134836 (2017)","DOI":"10.1109\/CVPR.2017.584"},{"key":"54_CR17","unstructured":"Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)"},{"key":"54_CR18","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 (2015)"},{"key":"54_CR19","doi-asserted-by":"crossref","unstructured":"Sun, X., et al.: Pix3d: dataset and methods for single-image 3D shape modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2974\u20132983 (2018)","DOI":"10.1109\/CVPR.2018.00314"},{"issue":"12","key":"54_CR20","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1007\/s11263-020-01347-6","volume":"128","author":"H Xie","year":"2020","unstructured":"Xie, H., Yao, H., Zhang, S., Zhou, S., Sun, W.: Pix2Vox++: multi-scale context-aware 3D object reconstruction from single and multiple images. Int. J. Comput. Vision 128(12), 2919\u20132935 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01347-6","journal-title":"Int. J. Comput. Vision"},{"key":"54_CR21","doi-asserted-by":"crossref","unstructured":"Mandikal, P., Babu, R.V.: Dense 3D point cloud reconstruction using a deep pyramid network. In: Proceedings-2019 IEEE Winter Conference on Applications of Computer Vision, pp. 1052\u20131060 (2019)","DOI":"10.1109\/WACV.2019.00117"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93046-2_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T08:10:26Z","timestamp":1655539826000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93046-2_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930455","9783030930462"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93046-2_54","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":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","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":"307","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":"105","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":"34% - 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":"3.2","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":"5.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}