{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:27:33Z","timestamp":1782574053197,"version":"3.54.5"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198236","type":"print"},{"value":"9783031198243","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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19824-3_11","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:14:32Z","timestamp":1668114872000},"page":"175-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Improving RGB-D Point Cloud Registration by\u00a0Learning Multi-scale Local Linear Transformation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2667-0937","authenticated-orcid":false,"given":"Ziming","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2832-3480","authenticated-orcid":false,"given":"Xiaoliang","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0155-4462","authenticated-orcid":false,"given":"Zhenghao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3516-0111","authenticated-orcid":false,"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8525-9163","authenticated-orcid":false,"given":"Lu","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2775-9730","authenticated-orcid":false,"given":"Dong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Ao, S., Hu, Q., Yang, B., Markham, A., Guo, Y.: SpinNet: learning a general surface descriptor for 3D point cloud registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11753\u201311762 (2021)","DOI":"10.1109\/CVPR46437.2021.01158"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., Lucey, S.: PointNetLK: robust and efficient point cloud registration using PointNet. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7163\u20137172 (2019)","DOI":"10.1109\/CVPR.2019.00733"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Bai, X., et al.: PointDSC: robust point cloud registration using deep spatial consistency. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15859\u201315869 (2021)","DOI":"10.1109\/CVPR46437.2021.01560"},{"key":"11_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","volume-title":"Computer Vision \u2013 ECCV 2006","author":"H Bay","year":"2006","unstructured":"Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404\u2013417. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_32"},{"issue":"2","key":"11_CR5","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"PJ Besl","year":"1992","unstructured":"Besl, P.J., Mckay, H.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239\u2013256 (1992)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1109\/TIP.2022.3140608","volume":"31","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Gu, S., Lu, G., Xu, D.: Exploiting intra-slice and inter-slice redundancy for learning-based lossless volumetric image compression. IEEE Trans. Image Process. 31, 1697\u20131707 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Lu, G., Hu, Z., Liu, S., Jiang, W., Xu, D.: LSVC: a learning-based stereo video compression framework. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6073\u20136082 (2022)","DOI":"10.1109\/CVPR52688.2022.00598"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Choy, C., Dong, W., Koltun, V.: Deep global registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2514\u20132523 (2020)","DOI":"10.1109\/CVPR42600.2020.00259"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3075\u20133084 (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Choy, C., Park, J., Koltun, V.: Fully convolutional geometric features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8958\u20138966 (2019)","DOI":"10.1109\/ICCV.2019.00905"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828\u20135839 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., Ilic, S.: PPFNet: global context aware local features for robust 3D point matching. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00028"},{"key":"11_CR13","unstructured":"Derpanis, K.G.: The Harris corner detector. York University 2 (2004)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 224\u2013236 (2018)","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"El Banani, M., Gao, L., Johnson, J.: UnsupervisedR &R: unsupervised point cloud registration via differentiable rendering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7129\u20137139 (2021)","DOI":"10.1109\/CVPR46437.2021.00705"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"El Banani, M., Johnson, J.: Bootstrap your own correspondences. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6433\u20136442 (2021)","DOI":"10.1109\/ICCV48922.2021.00637"},{"issue":"4","key":"11_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073592","volume":"36","author":"M Gharbi","year":"2017","unstructured":"Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. ACM Trans. Graph. (TOG) 36(4), 1\u201312 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"11_CR18","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1145\/3072959.3073592","volume":"36","author":"M Gharbi","year":"2017","unstructured":"Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. ACM Trans. Graph. 36(4), 118 (2017)","journal-title":"ACM Trans. Graph."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Gojcic, Z., Zhou, C., Wegner, J.D., Guibas, L.J., Birdal, T.: Learning multiview 3D point cloud registration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1759\u20131769 (2020)","DOI":"10.1109\/CVPR42600.2020.00183"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Wang, H., Hu, Q., Liu, H., Bennamoun, M.: Deep learning for 3D point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43, 4338\u20134364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., Schindler, K.: PREDATOR: registration of 3D point clouds with low overlap. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 4267\u20134276 (2021)","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Hui, T., Ngan, K.N.: Depth enhancement using RGB-D guided filtering, pp. 3832\u20133836 (2014)","DOI":"10.1109\/ICIP.2014.7025778"},{"key":"11_CR23","unstructured":"Katharopoulos, A., Vyas, A., Pappas, N., Fleuret, F.: Transformers are RNNs: fast autoregressive transformers with linear attention. In: International Conference on Machine Learning, pp. 5156\u20135165. PMLR (2020)"},{"key":"11_CR24","unstructured":"Ke, Y.: PCA-SIFT: a more distinctive representation for local image descriptors. In: 2004 Proceedings of the CVPR International Conference on Computer Vision and Pattern Recognition (2004)"},{"key":"11_CR25","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Kopf, J., Cohen, M.F., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Trans. Graph. (ToG) 26(3), 96-es (2007)","DOI":"10.1145\/1276377.1276497"},{"issue":"2","key":"11_CR27","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. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int. J. Comput. Vis."},{"key":"11_CR28","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1007\/978-3-642-33715-4_54","volume-title":"Computer Vision \u2013 ECCV 2012","author":"N Silberman","year":"2012","unstructured":"Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746\u2013760. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33715-4_54"},{"key":"11_CR30","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)"},{"issue":"10","key":"11_CR31","doi-asserted-by":"publisher","first-page":"4900","DOI":"10.1109\/TIP.2017.2722689","volume":"26","author":"B Rister","year":"2017","unstructured":"Rister, B., Horowitz, M.A., Rubin, D.L.: Volumetric image registration from invariant keypoints. IEEE Trans. Image Process. 26(10), 4900\u20134910 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, 6\u201313 November 2011 (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Song, S., Lichtenberg, S.P., Xiao, J.: SUN RGB-D: a RGB-D scene understanding benchmark suite. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 567\u2013576 (2015)","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"11_CR34","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: Proceedings of the International Conference on Intelligent Robot Systems (IROS), October 2012","DOI":"10.1109\/IROS.2012.6385773"},{"key":"11_CR35","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-030-58598-3_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Xia","year":"2020","unstructured":"Xia, X., et al.: Joint bilateral learning for real-time universal photorealistic style transfer. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 327\u2013342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_20"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Xu, B., Xu, Y., Yang, X., Jia, W., Guo, Y.: Bilateral grid learning for stereo matching networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12497\u201312506 (2021)","DOI":"10.1109\/CVPR46437.2021.01231"},{"key":"11_CR38","unstructured":"Yu, H., Li, F., Saleh, M., Busam, B., Ilic, S.: CoFiNet: reliable coarse-to-fine correspondences for robust pointcloud registration. In: Advances in Neural Information Processing Systems, vol. 34, pp. 23872\u201323884 (2021)"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Nie\u00dfner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3DMatch: learning local geometric descriptors from RGB-D reconstructions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1802\u20131811 (2017)","DOI":"10.1109\/CVPR.2017.29"}],"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\/978-3-031-19824-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:07:14Z","timestamp":1668211634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19824-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198236","9783031198243"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19824-3_11","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":"11 November 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)"}}]}}