{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:02:49Z","timestamp":1768003369328,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819996650","type":"print"},{"value":"9789819996667","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9666-7_21","type":"book-chapter","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T06:02:20Z","timestamp":1707199340000},"page":"314-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["QuadSampling: A Novel Sampling Method for\u00a0Remote Implicit Neural 3D Reconstruction Based on\u00a0Quad-Tree"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3053-9571","authenticated-orcid":false,"given":"Xu-Qiang","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4129-7704","authenticated-orcid":false,"given":"Yu-Ping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"issue":"6","key":"21_CR1","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","volume":"37","author":"C Campos","year":"2021","unstructured":"Campos, C., Elvira, R., Rodr\u00edguez, J.J.G., Montiel, J.M.M., Tard\u00f3s, J.D.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Trans. Robot. 37(6), 1874\u20131890 (2021)","journal-title":"IEEE Trans. Robot."},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: CVPR, pp. 5939\u20135948 (2019)","DOI":"10.1109\/CVPR.2019.00609"},{"key":"21_CR3","unstructured":"Chibane, J., Mir, A., Pons-Moll, G.: Neural unsigned distance fields for implicit function learning. In: NeurIPS (2020)"},{"key":"21_CR4","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":"21_CR5","doi-asserted-by":"crossref","unstructured":"Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Fujii, J. (ed.) Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1996, New Orleans, LA, USA, 4\u20139 August 1996, pp. 303\u2013312. ACM (1996)","DOI":"10.1145\/237170.237269"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T.A., Nie\u00dfner, M.: ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In: CVPR, pp. 2432\u20132443 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Deng, K., Liu, A., Zhu, J., Ramanan, D.: Depth-supervised NeRF: fewer views and faster training for free. In: CVPR, pp. 12872\u201312881 (2022)","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Dong, S., et al.: Multi-robot collaborative dense scene reconstruction. ACM Trans. Graph. 38(4), 84:1\u201384:16 (2019)","DOI":"10.1145\/3306346.3322942"},{"key":"21_CR9","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: CVPR, pp. 2463\u20132471 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Gkioxari, G., Johnson, J., Malik, J.: Mesh R-CNN. In: ICCV, pp. 9784\u20139794 (2019)","DOI":"10.1109\/ICCV.2019.00988"},{"issue":"11","key":"21_CR11","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.1109\/TVCG.2018.2868533","volume":"24","author":"S Golodetz","year":"2018","unstructured":"Golodetz, S., Cavallari, T., Lord, N.A., Prisacariu, V.A., Murray, D.W., Torr, P.H.S.: Collaborative large-scale dense 3D reconstruction with online inter-agent pose optimisation. IEEE Trans. Vis. Comput. Graph. 24(11), 2895\u20132905 (2018)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A Papier-M\u00e2ch\u00e9 approach to learning 3D surface generation. In: CVPR, pp. 216\u2013224 (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"issue":"3","key":"21_CR13","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10514-012-9321-0","volume":"34","author":"A Hornung","year":"2013","unstructured":"Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34(3), 189\u2013206 (2013)","journal-title":"Auton. Robots"},{"issue":"11","key":"21_CR14","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1109\/TVCG.2015.2459891","volume":"21","author":"O K\u00e4hler","year":"2015","unstructured":"K\u00e4hler, O., Prisacariu, V.A., Ren, C.Y., Sun, X., Torr, P.H.S., Murray, D.W.: Very high frame rate volumetric integration of depth images on mobile devices. IEEE Trans. Vis. Comput. Graph. 21(11), 1241\u20131250 (2015)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"issue":"1","key":"21_CR15","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/LRA.2015.2512958","volume":"1","author":"O K\u00e4hler","year":"2016","unstructured":"K\u00e4hler, O., Prisacariu, V.A., Valentin, J.P.C., Murray, D.W.: Hierarchical voxel block hashing for efficient integration of depth images. IEEE Robot. Autom. Lett. 1(1), 192\u2013197 (2016)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"21_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-030-01267-0_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Kanazawa","year":"2018","unstructured":"Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 386\u2013402. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01267-0_23"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Lin, C., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3D object reconstruction. In: AAAI, pp. 7114\u20137121 (2018)","DOI":"10.1609\/aaai.v32i1.12278"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Mescheder, L.M., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: CVPR, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Michalkiewicz, M., Pontes, J.K., Jack, D., Baktashmotlagh, M., Eriksson, A.P.: Implicit surface representations as layers in neural networks. In: ICCV, pp. 4742\u20134751 (2019)","DOI":"10.1109\/ICCV.2019.00484"},{"key":"21_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2011, Basel, Switzerland, 26\u201329 October 2011, pp. 127\u2013136 (2011)","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Nie\u00dfner, M., Zollh\u00f6fer, M., Izadi, S., Stamminger, M.: Real-time 3D reconstruction at scale using voxel hashing. ACM Trans. Graph. 32(6), 169:1\u2013169:11 (2013)","DOI":"10.1145\/2508363.2508374"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P.R., Straub, J., Newcombe, R.A., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: CVPR, pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Prokudin, S., Lassner, C., Romero, J.: Efficient learning on point clouds with basis point sets. In: ICCV, pp. 4331\u20134340 (2019)","DOI":"10.1109\/ICCV.2019.00443"},{"key":"21_CR25","unstructured":"Sitzmann, V., Martel, J.N.P., Bergman, A.W., Lindell, D.B., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: NeurIPS (2020)"},{"key":"21_CR26","unstructured":"Straub, J., et al.: The replica dataset: a digital replica of indoor spaces. CoRR abs\/1906.05797 (2019). http:\/\/arxiv.org\/abs\/1906.05797"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, Vilamoura, Algarve, Portugal, 7\u201312 October 2012, pp. 573\u2013580. IEEE (2012)","DOI":"10.1109\/IROS.2012.6385773"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: iMAP: implicit mapping and positioning in real-time. In: ICCV, pp. 6209\u20136218 (2021)","DOI":"10.1109\/ICCV48922.2021.00617"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Tang, D., et al.: Deep implicit volume compression. In: CVPR, pp. 1290\u20131300 (2020)","DOI":"10.1109\/CVPR42600.2020.00137"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Wei, Y., Liu, S., Rao, Y., Zhao, W., Lu, J., Zhou, J.: NerfingMVS: guided optimization of neural radiance fields for indoor multi-view stereo. In: ICCV, pp. 5590\u20135599 (2021)","DOI":"10.1109\/ICCV48922.2021.00556"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Wen, C., Zhang, Y., Li, Z., Fu, Y.: Pixel2mesh++: multi-view 3D mesh generation via deformation. In: ICCV, pp. 1042\u20131051 (2019)","DOI":"10.1109\/ICCV.2019.00113"},{"key":"21_CR32","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: NeurIPS, pp. 82\u201390 (2016)"},{"key":"21_CR33","unstructured":"Wu, Z., et al.: 3D shapenets: a deep representation for volumetric shapes. In: CVPR, pp. 1912\u20131920 (2015)"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Yang, G., Huang, X., Hao, Z., Liu, M., Belongie, S.J., Hariharan, B.: PointFlow: 3D point cloud generation with continuous normalizing flows. In: ICCV, pp. 4540\u20134549 (2019)","DOI":"10.1109\/ICCV.2019.00464"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Zhu, Z., et al.: NICE-SLAM: neural implicit scalable encoding for SLAM. In: CVPR, pp. 12776\u201312786 (2022)","DOI":"10.1109\/CVPR52688.2022.01245"}],"container-title":["Lecture Notes in Computer Science","Computer-Aided Design and Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9666-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T06:07:59Z","timestamp":1707199679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9666-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819996650","9789819996667"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9666-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"7 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CADGraphics","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer-Aided Design and Computer Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cadgraphics2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dmcv.sjtu.edu.cn\/cadgraphics2023\/","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":"editorialmanager","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"169","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":"23","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":"14% - 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","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","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)"}}]}}