{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:03:33Z","timestamp":1772823813164,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031820205","type":"print"},{"value":"9783031820212","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-82021-2_12","type":"book-chapter","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T09:45:47Z","timestamp":1740822347000},"page":"172-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DUE-MVSNet: Learning Multi-view Stereo Based on\u00a0Dual Uncertainty Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8161-0382","authenticated-orcid":false,"given":"Mingwei","family":"Cao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8634-3743","authenticated-orcid":false,"given":"Siqi","family":"Nian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9833-5684","authenticated-orcid":false,"given":"Jun","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,1]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11263-016-0902-9","volume":"120","author":"H Aan\u00e6s","year":"2016","unstructured":"Aan\u00e6s, H., Jensen, R.R., Vogiatzis, G., Tola, E., Dahl, A.B.: Large-scale data for multiple-view stereopsis. Int. J. Comput. Vis. 120, 153\u2013168 (2016)","journal-title":"Int. J. Comput. Vis."},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, W., Mordohai, P.: Learning the distribution of errors in stereo matching for joint disparity and uncertainty estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17235\u201317244 (2023)","DOI":"10.1109\/CVPR52729.2023.01653"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, S., et al.: Deep stereo using adaptive thin volume representation with uncertainty awareness. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2524\u20132534 (2020)","DOI":"10.1109\/CVPR42600.2020.00260"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Collins, R.T.: A space-sweep approach to true multi-image matching. In: Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 358\u2013363. IEEE (1996)","DOI":"10.1109\/CVPR.1996.517097"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 873\u2013881 (2015)","DOI":"10.1109\/ICCV.2015.106"},{"key":"12_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/978-3-031-23473-6_13","volume-title":"Advances in Computer Graphics","author":"S Gao","year":"2022","unstructured":"Gao, S., Li, Z., Wang, Z.: Cost volume pyramid network with multi-strategies range searching for multi-view stereo. In: Magnenat-Thalmann, N., et al. (eds.) Advances in Computer Graphics, pp. 157\u2013169. Springer, Cham (2022)"},{"issue":"Suppl 1","key":"12_CR9","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J., et al.: A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(Suppl 1), 1513\u20131589 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., Tan, P.: Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2020)","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3273\u20133282 (2019)","DOI":"10.1109\/CVPR.2019.00339"},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","volume":"110","author":"E H\u00fcllermeier","year":"2021","unstructured":"H\u00fcllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110, 457\u2013506 (2021)","journal-title":"Mach. Learn."},{"key":"12_CR13","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"4","key":"12_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073599","volume":"36","author":"A Knapitsch","year":"2017","unstructured":"Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph. (ToG) 36(4), 1\u201313 (2017)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Li, J., Lu, Z., Wang, Y., Xiao, J., Wang, Y.: NR-MVSNet: learning multi-view stereo based on normal consistency and depth refinement. IEEE Trans. Image Process. (2023)","DOI":"10.1109\/TIP.2023.3272170"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Liu, T., Ye, X., Zhao, W., Pan, Z., Shi, M., Cao, Z.: When epipolar constraint meets non-local operators in multi-view stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 18088\u201318097 (2023)","DOI":"10.1109\/ICCV51070.2023.01658"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Luo, K., Guan, T., Ju, L., Wang, Y., Chen, Z., Luo, Y.: Attention-aware multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1590\u20131599 (2020)","DOI":"10.1109\/CVPR42600.2020.00166"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104\u20134113 (2016)","DOI":"10.1109\/CVPR.2016.445"},{"issue":"11","key":"12_CR20","doi-asserted-by":"publisher","first-page":"7796","DOI":"10.1109\/TCSVT.2022.3183836","volume":"32","author":"W Su","year":"2022","unstructured":"Su, W., Xu, Q., Tao, W.: Uncertainty guided multi-view stereo network for depth estimation. IEEE Trans. Circ. Syst. Vid. Technol. 32(11), 7796\u20137808 (2022)","journal-title":"IEEE Trans. Circ. Syst. Vid. Technol."},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Wang, L., Gong, Y., Ma, X., Wang, Q., Zhou, K., Chen, L.: IS-MVSNet: importance sampling-based MVSNet. In: European Conference on Computer Vision, pp. 668\u2013683. Springer (2022)","DOI":"10.1007\/978-3-031-19824-3_39"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: MVSTER: epipolar transformer for efficient multi-view stereo. In: European Conference on Computer Vision, pp. 573\u2013591. Springer (2022)","DOI":"10.1007\/978-3-031-19821-2_33"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Wei, Z., Zhu, Q., Min, C., Chen, Y., Wang, G.: AA-RMVSNet: adaptive aggregation recurrent multi-view stereo network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6187\u20136196 (2021)","DOI":"10.1109\/ICCV48922.2021.00613"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Xu, Q., Tao, W.: Learning inverse depth regression for multi-view stereo with correlation cost volume. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 12508\u201312515 (2020)","DOI":"10.1609\/aaai.v34i07.6939"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Yang, J., Mao, W., Alvarez, J.M., Liu, M.: Cost volume pyramid based depth inference for multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4877\u20134886 (2020)","DOI":"10.1109\/CVPR42600.2020.00493"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: MVSNet: depth inference for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 767\u2013783 (2018)","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., Quan, L.: Recurrent MVSNet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5525\u20135534 (2019)","DOI":"10.1109\/CVPR.2019.00567"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Yao, Y., et al.: BlendedMVS: a large-scale dataset for generalized multi-view stereo networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1790\u20131799 (2020)","DOI":"10.1109\/CVPR42600.2020.00186"},{"key":"12_CR29","unstructured":"Yi, P., Tang, S., Yao, J.: DDR-Net: learning multi-stage multi-view stereo with dynamic depth range. arXiv preprint arXiv:2103.14275 (2021)"},{"key":"12_CR30","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.isprsjprs.2021.03.010","volume":"175","author":"A Yu","year":"2021","unstructured":"Yu, A., et al.: Attention aware cost volume pyramid based multi-view stereo network for 3D reconstruction. ISPRS J. Photogramm. Remote. Sens. 175, 448\u2013460 (2021)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"1","key":"12_CR31","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s11263-022-01697-3","volume":"131","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Li, S., Luo, Z., Fang, T., Yao, Y.: Vis-MVSNet: visibility-aware multi-view stereo network. Int. J. Comput. Vis. 131(1), 199\u2013214 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Peng, R., Hu, Y., Wang, R.: GeoMVSNet: learning multi-view stereo with geometry perception. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21508\u201321518 (2023)","DOI":"10.1109\/CVPR52729.2023.02060"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82021-2_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T09:46:09Z","timestamp":1740822369000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82021-2_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031820205","9783031820212"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82021-2_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Geneva","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"41","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cgs-network.org\/cgi24\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}