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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>Many multi-view stereo (MVS) networks with a cascaded structure can effectively estimate depth while saving memory. However, the accuracy of the depth map in the fine stage depends on the depth map estimated in the coarse stage. Additionally, the multi-stage depth maps generated by the cascaded structure are used to compute losses but are not reused, resulting in a loss of inter-stage differentiation information. To address these issues, we propose a dual-uncertainty estimation MVS method that learns an MVS network based on adjacent stage and pair-wise stage uncertainty estimation, named APMVS. The core of the proposed APMVS is to employ dual-uncertainty estimation to mitigate the adverse effects of the cascaded structure. Specifically, it involves two estimation modules: adjacent stage uncertainty (ASU) and pair-wise stage uncertainty (PSU). The ASU estimation module dynamically adjusts the depth-hypothesis range by leveraging uncertainty from the previous stage, thereby improving the accuracy of depth-map prediction in the current stage. The PSU estimation module estimates the uncertainty between each pair of stages. Thus, regions with high uncertainty have minimal impact. We evaluate the proposed APMVS on the DTU, Tanks and Temples, and BlendedMVS datasets. Experimental results show that our method achieves superior reconstruction quality compared with other state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3799231","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T12:56:23Z","timestamp":1772542583000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["APMVS: Learning Multi-View Stereo Based on Adjacent Stage and Pair-Wise Stage Uncertainty Estimation"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8161-0382","authenticated-orcid":false,"given":"Mingwei","family":"Cao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8634-3743","authenticated-orcid":false,"given":"Siqi","family":"Nian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0957-0084","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5300-0683","authenticated-orcid":false,"given":"Haifeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-9734","authenticated-orcid":false,"given":"Feng","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Software, Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1938-8574","authenticated-orcid":false,"given":"Ruijun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2525-3074","authenticated-orcid":false,"given":"Zhihan","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0902-9"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3177756","article-title":"Full 3D reconstruction of non-rigidly deforming objects","volume":"14","author":"Afzal Hassan","year":"2018","unstructured":"Hassan Afzal, Djamila Aouada, Bruno Mirbach, and Bj\u00f6rn E. 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In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00310"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00166"},{"key":"e_1_3_1_46_2","first-page":"19067","volume-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","author":"Pan Feiyu","year":"2025","unstructured":"Feiyu Pan, Hao Fang, Fangkai Li, Yanyu Xu, Yawei Li, Luca Benini, and Xiankai Lu. 2025. Semantic and sequential alignment for referring video object segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference, 19067\u201319076."},{"key":"e_1_3_1_47_2","first-page":"3611","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Ren Chunlin","year":"2023","unstructured":"Chunlin Ren, Qingshan Xu, Shikun Zhang, and Jiaqi Yang. 2023. Hierarchical prior mining for non-local multi-view stereo. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 3611\u20133620."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.445"},{"key":"e_1_3_1_49_2","first-page":"501","volume-title":"Proceedings of European Conference on Computer Vision","author":"Sch\u00f6nberger Johannes L.","year":"2016","unstructured":"Johannes L. Sch\u00f6nberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. 2016. Pixelwise view selection for unstructured multi-view stereo. In Proceedings of European Conference on Computer Vision. Springer, 501\u2013518."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3521674"},{"key":"e_1_3_1_51_2","unstructured":"Oriane Sim\u00e9oni Huy V. Vo Maximilian Seitzer Federico Baldassarre Maxime Oquab Cijo Jose Vasil Khalidov Marc Szafraniec Seungeun Yi Micha\u00ebl Ramamonjisoa et al. 2025. DINOv3. arXiv:2508.10104. Retrieved from https:\/\/arxiv.org\/abs\/2508.10104"},{"key":"e_1_3_1_52_2","first-page":"1","volume-title":"Proceedings of 2007 IEEE 11th International Conference on Computer Vision","author":"Sinha Sudipta N.","year":"2007","unstructured":"Sudipta N. Sinha, Philippos Mordohai, and Marc Pollefeys. 2007. Multi-view stereo via graph cuts on the dual of an adaptive tetrahedral mesh. In Proceedings of 2007 IEEE 11th International Conference on Computer Vision. IEEE, 1\u20138."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3183836"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491224"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3193421"},{"key":"e_1_3_1_56_2","first-page":"4531","volume-title":"Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition","author":"Ulusoy Ali Osman","year":"2017","unstructured":"Ali Osman Ulusoy, Michael J. Black, and Andreas Geiger. 2017. Semantic multi-view stereo: Jointly estimating objects and voxels. In Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 4531\u20134540."},{"key":"e_1_3_1_57_2","first-page":"8606","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Fangjinhua","year":"2022","unstructured":"Fangjinhua Wang, Silvano Galliani, Christoph Vogel, and Marc Pollefeys. 2022. IterMVS: Iterative probability estimation for efficient multi-view stereo. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 8606\u20138615."},{"key":"e_1_3_1_58_2","first-page":"668","volume-title":"Proceedings of European Conference on Computer Vision","author":"Wang Likang","year":"2022","unstructured":"Likang Wang, Yue Gong, Xinjun Ma, Qirui Wang, Kaixuan Zhou, and Lei Chen. 2022. IS-MVSNet: Importance sampling-based MVSNet. In Proceedings of European Conference on Computer Vision. 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