{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:43Z","timestamp":1760245063425,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"],"award-info":[{"award-number":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"],"award-info":[{"award-number":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"]}]},{"name":"Natural Science Special Research Fund of Guizhou University","award":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"],"award-info":[{"award-number":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"]}]},{"name":"Guizhou University Cultivation Project","award":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"],"award-info":[{"award-number":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"]}]},{"name":"Program of Introducing Talents of Discipline to Universities of China","award":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"],"award-info":[{"award-number":["62162008","62006046","32125033","31960548","ZK[2022]-108","2021-24","2021-55","D20023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Efficient dense reconstruction of objects or scenes has substantial practical implications, which can be applied to different 3D tasks (for example, robotics and autonomous driving). However, because of the expensive hardware required and the overall complexity of the all-around scenarios, efficient dense reconstruction using lightweight multi-view stereo methods has received much attention from researchers. The technological challenge of efficient dense reconstruction is maintaining low memory usage while rapidly and reliably acquiring depth maps. Most of the current efficient multi-view stereo (MVS) methods perform poorly in efficient dense reconstruction, this poor performance is mainly due to weak generalization performance and unrefined object edges in the depth maps. To this end, we propose EMO-MVS, which aims to accomplish multi-view stereo tasks with high efficiency, which means low-memory consumption, high accuracy, and excellent generalization performance. In detail, we first propose an iterative variable optimizer to accurately estimate depth changes. Then, we design a multi-level absorption unit that expands the receptive field, which efficiently generates an initial depth map. In addition, we propose an error-aware enhancement module, enhancing the initial depth map by optimizing the projection error between multiple views. We have conducted extensive experiments on challenging datasets Tanks and Temples and DTU, and also performed a complete visualization comparison on the BlenedMVS validation set (which contains many aerial scene images), achieving promising performance on all datasets. Among the lightweight MVS methods with low-memory consumption and fast inference speed, our F-score on the online Tanks and Temples intermediate benchmark is the highest, which shows that we have the best competitiveness in terms of balancing the performance and computational cost.<\/jats:p>","DOI":"10.3390\/rs14236085","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T02:24:46Z","timestamp":1669861486000},"page":"6085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["EMO-MVS: Error-Aware Multi-Scale Iterative Variable Optimizer for Efficient Multi-View Stereo"],"prefix":"10.3390","volume":"14","author":[{"given":"Huizhou","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Physics & Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3789-7451","authenticated-orcid":false,"given":"Haoliang","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6269-0196","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Physics & Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510000, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Physics & Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510000, China"},{"name":"Guangdong Provincial Key Laboratory of Information Photonics Technology, Guangzhou 510000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gefei","family":"Hao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusheng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informations, Tongji University, Shanghai 200000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7178-9817","authenticated-orcid":false,"given":"Zhen","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informations, Tongji University, Shanghai 200000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","article-title":"A survey of autonomous driving: Common practices and emerging technologies","volume":"8","author":"Yurtsever","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Burdea, G.C., and Coiffet, P. (2003). Virtual Reality Technology, John Wiley & Sons.","DOI":"10.1162\/105474603322955950"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MRA.2007.339608","article-title":"The evolution of robotics research","volume":"14","author":"Garcia","year":"2007","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Geiger, A., Ziegler, J., and Stiller, C. (2011, January 5\u20139). Stereoscan: Dense 3d reconstruction in real-time. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940405"},{"key":"ref_5","unstructured":"Bleyer, M., Rhemann, C., and Rother, C. (September, January 29). Patchmatch Stereo-Stereo Matching with Slanted Support Windows. Proceedings of the British Machine Vision Conference, Vienna, Austria."},{"key":"ref_6","first-page":"56","article-title":"A plane-sweep strategy for the 3D reconstruction of buildings from multiple images","volume":"33","author":"Baillard","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","article-title":"Accurate, dense, and robust multiview stereopsis","volume":"32","author":"Furukawa","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Galliani, S., Lasinger, K., and Schindler, K. (2015, January 7\u201313). Massively parallel multiview stereopsis by surface normal diffusion. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.106"},{"key":"ref_9","unstructured":"Schonberger, J.L., and Frahm, J.M. (July, January 26). Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, Q., and Tao, W. (2019, January 15\u201320). Multi-scale geometric consistency guided multi-view stereo. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00563"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Fang, T., and Quan, L. (2018, January 8\u201314). Mvsnet: Depth inference for unstructured multi-view stereo. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., and Tan, P. (2020, January 14\u201319). Cascade cost volume for high-resolution multi-view stereo and stereo matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, J., Mao, W., Alvarez, J.M., and Liu, M. (2020, January 13\u201319). Cost volume pyramid based depth inference for multi-view stereo. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.00493"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., and Quan, L. (2019, January 15\u201320). Recurrent mvsnet for high-resolution multi-view stereo depth inference. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00567"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, X., Gong, Y., Wang, Q., Huang, J., Chen, L., and Yu, F. (2021, January 11\u201317). EPP-MVSNet: Epipolar-assembling based Depth Prediction for Multi-view Stereo. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Online.","DOI":"10.1109\/ICCV48922.2021.00568"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Stathopoulou, E.K., Battisti, R., Cernea, D., Remondino, F., and Georgopoulos, A. (2021). Semantically derived geometric constraints for MVS reconstruction of textureless areas. Remote Sens., 13.","DOI":"10.3390\/rs13061053"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7549","DOI":"10.1109\/TIP.2020.3004249","article-title":"Metasearch: Incremental product search via deep meta-learning","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., and Deng, J. (2021, January 1\u20133). Raft-stereo: Multilevel recurrent field transforms for stereo matching. Proceedings of the 2021 International Conference on 3D Vision (3DV), Online.","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, H., and Zhang, J. (2020, January 13\u201319). Aanet: Adaptive aggregation network for efficient stereo matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chang, J.R., and Chen, Y.S. (2018, January 18\u201322). Pyramid stereo matching network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, Z., and Gao, S. (2020, January 18\u201322). Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.00202"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yan, J., Wei, Z., Yi, H., Ding, M., Zhang, R., Chen, Y., Wang, G., and Tai, Y.W. (2020, January 23\u201328). Dense hybrid recurrent multi-view stereo net with dynamic consistency checking. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58548-8_39"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, F., Galliani, S., Vogel, C., Speciale, P., and Pollefeys, M. (2021, January 19\u201325). Patchmatchnet: Learned multi-view patchmatch stereo. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.01397"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, F., Galliani, S., Vogel, C., and Pollefeys, M. (2022, January 19\u201320). IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00841"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Teed, Z., and Deng, J. (2020, January 23\u201328). Raft: Recurrent all-pairs field transforms for optical flow. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, Z., Ren, Z., Shan, Q., and Huang, Q. (2022, January 19\u201320). Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00838"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tanduo, B., Martino, A., Balletti, C., and Guerra, F. (2022). New Tools for Urban Analysis: A SLAM-Based Research in Venice. Remote Sens., 14.","DOI":"10.3390\/rs14174325"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, G., Wang, Q., Huang, Y., Tian, J., Li, H., and Wang, Y. (2022). True2 Orthoimage Map Generation. Remote Sens., 14.","DOI":"10.3390\/rs14174396"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kutulakos, K.N., and Seitz, S.M. (1999, January 20\u201325). A theory of shape by space carving. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.791235"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1023\/A:1008176507526","article-title":"Photorealistic scene reconstruction by voxel coloring","volume":"35","author":"Seitz","year":"1999","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ulusoy, A.O., Black, M.J., and Geiger, A. (2017, January 21\u201326). Semantic multi-view stereo: Jointly estimating objects and voxels. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.482"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1109\/TPAMI.2005.44","article-title":"A quasi-dense approach to surface reconstruction from uncalibrated images","volume":"27","author":"Lhuillier","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, G., Gon\u00e7alves, D., G\u00f3mez-Guti\u00e9rrez, \u00c1., Andriolo, U., and P\u00e9rez-Alv\u00e1rez, J.A. (2021). 3D reconstruction of coastal cliffs from fixed-wing and multi-rotor uas: Impact of sfm-mvs processing parameters, image redundancy and acquisition geometry. Remote Sens., 13.","DOI":"10.3390\/rs13061222"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1016\/j.scib.2020.04.006","article-title":"Graph attention convolutional neural network model for chemical poisoning of honey bees\u2019 prediction","volume":"65","author":"Wang","year":"2020","journal-title":"Sci. Bull."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Campbell, N.D., Vogiatzis, G., Hern\u00e1ndez, C., and Cipolla, R. (2008, January 12\u201318). Using multiple hypotheses to improve depth-maps for multi-view stereo. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88682-2_58"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Frahm, J.M., and Pollefeys, M. (2016, January 11\u201314). Pixelwise view selection for unstructured multi-view stereo. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_31"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, Z., Jiang, H., Sun, H., Bao, H., and Zhang, G. (2021). DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes. Remote Sens., 13.","DOI":"10.3390\/rs13224569"},{"key":"ref_38","unstructured":"Zhang, J., Yao, Y., Li, S., Luo, Z., and Fang, T. (2020). Visibility-aware multi-view stereo network. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wei, Z., Zhu, Q., Min, C., Chen, Y., and Wang, G. (2021, January 10\u201317). Aa-rmvsnet: Adaptive aggregation recurrent multi-view stereo network. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00613"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yuan, W., Zhu, Q., Zhang, H., Liu, X., Wang, Y., and Liu, X. (2022, January 21\u201324). Transmvsnet: Global context-aware multi-view stereo network with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00839"},{"key":"ref_41","unstructured":"Gu, X., Yuan, W., Dai, Z., Tang, C., Zhu, S., and Tan, P. (2021). Dro: Deep recurrent optimizer for structure-from-motion. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3054739","article-title":"Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration","volume":"36","author":"Dai","year":"2017","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., and Davison, A. (2011, January 16\u201319). KinectFusion: Real-time 3D reconstruction and interaction using a moving depth camera. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, Santa Barbara, CA, USA.","DOI":"10.1145\/2047196.2047270"},{"key":"ref_44","unstructured":"Xu, Q., and Tao, W. (2020). Pvsnet: Pixelwise visibility-aware multi-view stereo network. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, K., Yang, W., Wang, X., and Li, H. (2019, January 16\u201320). Group-wise correlation stereo network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00339"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s11263-016-0902-9","article-title":"Large-scale data for multiple-view stereopsis","volume":"120","author":"Jensen","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ji, M., Gall, J., Zheng, H., Liu, Y., and Fang, L. (2017, January 22\u201329). Surfacenet: An end-to-end 3d neural network for multiview stereopsis. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.253"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., and Quan, L. (2020, January 20\u201325). Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR42600.2020.00186"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073599","article-title":"Tanks and temples: Benchmarking large-scale scene reconstruction","volume":"36","author":"Knapitsch","year":"2017","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Peng, R., Wang, R., Wang, Z., Lai, Y., and Wang, R. (2022). Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation and Focal Loss. arXiv.","DOI":"10.1109\/CVPR52688.2022.00845"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hartmann, W., Galliani, S., Havlena, M., Van Gool, L., and Schindler, K. (2017, January 22\u201329). Learned multi-patch similarity. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.176"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cheng, S., Xu, Z., Zhu, S., Li, Z., Li, L.E., Ramamoorthi, R., and Su, H. (2020, January 13\u201319). Deep stereo using adaptive thin volume representation with uncertainty awareness. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00260"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Moulon, P., Monasse, P., Perrot, R., and Marlet, R. (2016). Openmvg: Open multiple view geometry. International Workshop on Reproducible Research in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-56414-2_5"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Xi, J., Shi, Y., Wang, Y., Guo, Y., and Xu, K. (2022, January 21\u201324). RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00840"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6085\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:46Z","timestamp":1760146246000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":54,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236085"],"URL":"https:\/\/doi.org\/10.3390\/rs14236085","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,30]]}}}