{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T01:33:47Z","timestamp":1793151227962,"version":"4.5.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200496","type":"print"},{"value":"9783031200502","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20050-2_19","type":"book-chapter","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T18:09:58Z","timestamp":1666894198000},"page":"318-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["MINER: Multiscale Implicit Neural Representation"],"prefix":"10.1007","author":[{"given":"Vishwanath","family":"Saragadam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasper","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guha","family":"Balakrishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard G.","family":"Baraniuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashok","family":"Veeraraghavan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"19_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1007\/978-3-030-58526-6_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"R Chabra","year":"2020","unstructured":"Chabra, R., et al.: Deep local shapes: learning local SDF priors for detailed 3D reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 608\u2013625. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_36"},{"key":"19_CR2","volume-title":"Nerv: Neural Representations for Videos","author":"H Chen","year":"2021","unstructured":"Chen, H., He, B., Wang, H., Ren, Y., Lim, S.N., Shrivastava, A.: Nerv: Neural Representations for Videos. Adv. Neural Info, Processing Systems (2021)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Wang, X.: Learning continuous image representation with local implicit image function. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00852"},{"issue":"1","key":"19_CR4","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/S0734-189X(86)80031-7","volume":"36","author":"CH Chien","year":"1986","unstructured":"Chien, C.H., Aggarwal, J.K.: Volume\/surface octrees for the representation of three-dimensional objects. Comput. Vision Graph. Image Process. 36(1), 100\u2013113 (1986)","journal-title":"Comput. Vision Graph. Image Process."},{"issue":"9","key":"19_CR5","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1109\/TSP.2003.815389","volume":"51","author":"MN Do","year":"2003","unstructured":"Do, M.N., Vetterli, M.: Framing pyramids. IEEE Trans. Signal Process. 51(9), 2329\u20132342 (2003)","journal-title":"IEEE Trans. Signal Process."},{"key":"19_CR6","first-page":"8820","volume":"34","author":"A Hertz","year":"2021","unstructured":"Hertz, A., Perel, O., Giryes, R., Sorkine-Hornung, O., Cohen-Or, D.: Sape: spatially-adaptive progressive encoding for neural optimization. Adv. Neural Info. Process. Syst. 34, 8820\u20138832 (2021)","journal-title":"Adv. Neural Info. Process. Syst."},{"issue":"3","key":"19_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2487228.2487237","volume":"32","author":"M Kazhdan","year":"2013","unstructured":"Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. 32(3), 1\u201313 (2013)","journal-title":"ACM Trans. Graph."},{"key":"19_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference Learning Representations (2015)"},{"issue":"4","key":"19_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459795","volume":"40","author":"A Kuznetsov","year":"2021","unstructured":"Kuznetsov, A., Mullia, K., Xu, Z., Ha\u0161an, M., Ramamoorthi, R.: NeuMIP: Multi-resolution neural materials. ACM Trans. Graphics 40(4), 1\u201313 (2021)","journal-title":"ACM Trans. Graphics"},{"issue":"4","key":"19_CR10","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH 21(4), 163\u2013169 (1987)","journal-title":"ACM SIGGRAPH"},{"issue":"2","key":"19_CR11","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Intl. J. Comput. Vision 60(2), 91\u2013110 (2004)","journal-title":"Intl. J. Comput. Vision"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Mairal, J., Sapiro, G., Elad, M.: Multiscale sparse image representation with learned dictionaries. In: IEEE International Conference Image Processing (ICIP), vol. 3, pp. III-105 (2007)","DOI":"10.1109\/ICIP.2007.4379257"},{"key":"19_CR13","unstructured":"Martel, J.N., Lindell, D.B., Lin, C.Z., Chan, E.R., Monteiro, M., Wetzstein, G.: Acorn: Adaptive coordinate networks for neural scene representation. arXiv preprint arXiv:2105.02788 (2021)"},{"issue":"2","key":"19_CR14","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/0146-664X(82)90104-6","volume":"19","author":"D Meagher","year":"1982","unstructured":"Meagher, D.: Geometric modeling using octree encoding. Comput. Graph. Image Process. 19(2), 129\u2013147 (1982)","journal-title":"Comput. Graph. Image Process."},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Mehta, I., Gharbi, M., Barnes, C., Shechtman, E., Ramamoorthi, R., Chandraker, M.: Modulated periodic activations for generalizable local functional representations. In: IEEE International Conference Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01395"},{"key":"19_CR16","doi-asserted-by":"crossref","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: IEEE European Conf. Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Park, J.Y., Wakin, M.B.: A multiscale framework for compressive sensing of video. In: Picture Coding Symposium (2009)","DOI":"10.1109\/PCS.2009.5167440"},{"key":"19_CR18","unstructured":"Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advance Neural Information Processing Systems (2019)"},{"key":"19_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-030-58580-8_31","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Peng","year":"2020","unstructured":"Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.: Convolutional occupancy networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 523\u2013540. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_31"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Reiser, C., Peng, S., Liao, Y., Geiger, A.: Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. arXiv preprint arXiv:2103.13744 (2021)","DOI":"10.1109\/ICCV48922.2021.01407"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. In: Fundamental Papers in Wavelet Theory, pp. 861\u2013878 (2009)","DOI":"10.1515\/9781400827268.861"},{"key":"19_CR22","volume-title":"Implicit neural representations with periodic activation functions","author":"V Sitzmann","year":"2020","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. Adv. Neural Info, Processing Systems (2020)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Srinivasan, P.P., Deng, B., Zhang, X., Tancik, M., Mildenhall, B., Barron, J.T.: Nerv: Neural reflectance and visibility fields for relighting and view synthesis. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00741"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Sulam, J., Ophir, B., Elad, M.: Image denoising through multi-scale learnt dictionaries. In: IEEE International Conference Image Processing (ICIP) (2014)","DOI":"10.1109\/ICIP.2014.7025162"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Sun, Y., Liu, J., Xie, M., Wohlberg, B., Kamilov, U.S.: Coil: Coordinate-based internal learning for imaging inverse problems. arXiv preprint arXiv:2102.05181 (2021)","DOI":"10.1109\/TCI.2021.3125564"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Takikawa, T., et al.: Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01120"},{"key":"19_CR27","volume-title":"Fourier features let networks learn high frequency functions in low dimensional domains","author":"M Tancik","year":"2020","unstructured":"Tancik, M.: Fourier features let networks learn high frequency functions in low dimensional domains. Adv. Neural Info, Processing Systems (2020)"},{"issue":"12","key":"19_CR28","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.1109\/83.887976","volume":"9","author":"P Th\u00e9venaz","year":"2000","unstructured":"Th\u00e9venaz, P., Unser, M.: Optimization of mutual information for multiresolution image registration. IEEE Trans. Image Processing 9(12), 2083\u20132099 (2000)","journal-title":"IEEE Trans. Image Processing"},{"issue":"1","key":"19_CR29","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/BF01421489","volume":"14","author":"J Weber","year":"1995","unstructured":"Weber, J., Malik, J.: Robust computation of optical flow in a multi-scale differential framework. Intl. J. Comput. Vision 14(1), 67\u201381 (1995)","journal-title":"Intl. J. Comput. Vision"},{"key":"19_CR30","unstructured":"Yifan, W., Rahmann, L., Sorkine-Hornung, O.: Geometry-consistent neural shape representation with implicit displacement fields. arXiv preprint arXiv:2106.05187 (2021)"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. arXiv preprint arXiv:2103.14024 (2021)","DOI":"10.1109\/ICCV48922.2021.00570"}],"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\/978-3-031-20050-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T01:14:53Z","timestamp":1793150093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-20050-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200496","9783031200502"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20050-2_19","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":"28 October 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}