{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:15:30Z","timestamp":1775578530965,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030110147","type":"print"},{"value":"9783030110154","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-11015-4_50","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T06:42:47Z","timestamp":1548312167000},"page":"662-674","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction from a Single Image"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6257-7417","authenticated-orcid":false,"given":"Priyanka","family":"Mandikal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2420-3684","authenticated-orcid":false,"given":"K. L.","family":"Navaneet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1926-1804","authenticated-orcid":false,"given":"R. Venkatesh","family":"Babu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"50_CR1","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"50_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":"50_CR3","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.: A point set generation network for 3D object reconstruction from a single image. In: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 38 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"50_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/978-3-319-46466-4_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"R Girdhar","year":"2016","unstructured":"Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: Learning a predictable and generative vector representation for objects. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 484\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_29"},{"key":"50_CR5","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B., Aubry, M.: AtlasNet: a papier-M\u00e2ch\u00e9 approach to learning 3D surface generation. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"50_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988. IEEE (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"50_CR7","doi-asserted-by":"crossref","unstructured":"Kalogerakis, E., Averkiou, M., Maji, S., Chaudhuri, S.: 3D shape segmentation with projective convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.702"},{"issue":"8","key":"50_CR8","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1111\/cogs.12470","volume":"41","author":"SE Koopman","year":"2017","unstructured":"Koopman, S.E., Mahon, B.Z., Cantlon, J.F.: Evolutionary constraints on human object perception. Cogn. Sci. 41(8), 2126\u20132148 (2017)","journal-title":"Cogn. Sci."},{"key":"50_CR9","unstructured":"Li, Y., Bu, R., Sun, M., Chen, B.: PointCNN. arXiv preprint arXiv:1801.07791 (2018)"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"50_CR11","doi-asserted-by":"crossref","unstructured":"Mandikal, P., Navaneet, K.L., Agarwal, M., Babu, R.V.: 3D-LMNet: latent embedding matching for accurate and diverse 3D point cloud reconstruction from a single image. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)","DOI":"10.1007\/978-3-030-11015-4_50"},{"key":"50_CR12","doi-asserted-by":"crossref","unstructured":"Muralikrishnan, S., Kim, V.G., Chaudhuri, S.: Tags2Parts: discovering semantic regions from shape tags. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2926\u20132935 (2018)","DOI":"10.1109\/CVPR.2018.00309"},{"issue":"2","key":"50_CR13","first-page":"4","volume":"1","author":"CR Qi","year":"2017","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. Proc. Comput. Vis. Pattern Recogn. (CVPR) 1(2), 4 (2017)","journal-title":"Proc. Comput. Vis. Pattern Recogn. (CVPR)"},{"key":"50_CR14","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, pp. 5105\u20135114 (2017)"},{"key":"50_CR15","doi-asserted-by":"crossref","unstructured":"Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 190\u2013198. IEEE (2017)","DOI":"10.1109\/CVPR.2017.28"},{"key":"50_CR16","doi-asserted-by":"crossref","unstructured":"Su, H., et al.: SplatNet: sparse lattice networks for point cloud processing. arXiv preprint arXiv:1802.08275 (2018)","DOI":"10.1109\/CVPR.2018.00268"},{"key":"50_CR17","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: CVPR, vol. 1, p. 3 (2017)","DOI":"10.1109\/CVPR.2017.30"},{"key":"50_CR18","unstructured":"Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, B., Tenenbaum, J.: MarrNet: 3D shape reconstruction via 2.5 d sketches. In: Advances In Neural Information Processing Systems, pp. 540\u2013550 (2017)"},{"key":"50_CR19","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems, pp. 82\u201390 (2016)"},{"key":"50_CR20","unstructured":"Yan, X., Yang, J., Yumer, E., Guo, Y., Lee, H.: Perspective transformer nets: learning single-view 3D object reconstruction without 3D supervision. In: Advances in Neural Information Processing Systems, pp. 1696\u20131704 (2016)"},{"issue":"6","key":"50_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2980238","volume":"35","author":"Li Yi","year":"2016","unstructured":"Yi, L., et al.: A scalable active framework for region annotation in 3D shape collections. In: SIGGRAPH Asia (2016)","journal-title":"ACM Transactions on Graphics"},{"key":"50_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, R., Galoogahi, H.K., Wang, C., Lucey, S.: Rethinking reprojection: closing the loop for pose-aware shape reconstruction from a single image. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 57\u201365. IEEE (2017)","DOI":"10.1109\/ICCV.2017.16"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11015-4_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T01:36:52Z","timestamp":1674351412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11015-4_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110147","9783030110154"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11015-4_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","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":"Munich","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}