{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:23:50Z","timestamp":1775870630878,"version":"3.50.1"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031784552","type":"print"},{"value":"9783031784569","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"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\/978-3-031-78456-9_9","type":"book-chapter","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T11:23:59Z","timestamp":1733138639000},"page":"130-144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Visibility-Aware Pixelwise View Selection for\u00a0Multi-View Stereo Matching"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0764-1669","authenticated-orcid":false,"given":"Zhentao","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8403-7547","authenticated-orcid":false,"given":"Yukun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5820-5381","authenticated-orcid":false,"given":"Minglun","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","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. 1\u201316 (2016)","DOI":"10.1007\/s11263-016-0902-9"},{"issue":"3","key":"9_CR2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/1531326.1531330","volume":"28","author":"C Barnes","year":"2009","unstructured":"Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patchmatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24 (2009)","journal-title":"ACM Trans. Graph."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Bleyer, M., Rhemann, C., Rother, C.: Patchmatch stereo-stereo matching with slanted support windows. In: The British Machine Vision Conference (BMVC), vol.\u00a011, pp. 1\u201311 (2011)","DOI":"10.5244\/C.25.14"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Campbell, N.D., Vogiatzis, G., Hern\u00e1ndez, C., Cipolla, R.: Using multiple hypotheses to improve depth-maps for multi-view stereo. In: European Conference on Computer Vision, pp. 766\u2013779. Springer, Heidelberg (2008)","DOI":"10.1007\/978-3-540-88682-2_58"},{"key":"9_CR5","unstructured":"Cao, C., Ren, X., Fu, Y.: Mvsformer: multi-view stereo by learning robust image features and temperature-based depth. Trans. Mach. Learn. Res. (2023)"},{"issue":"10","key":"9_CR6","doi-asserted-by":"publisher","first-page":"3695","DOI":"10.1109\/TPAMI.2020.2988729","volume":"43","author":"R Chen","year":"2021","unstructured":"Chen, R., Han, S., Xu, J., Su, H.: Visibility-aware point-based multi-view stereo network. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3695\u20133708 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2988729","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5939\u20135948 (2019)","DOI":"10.1109\/CVPR.2019.00609"},{"key":"9_CR8","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":"9_CR9","doi-asserted-by":"crossref","unstructured":"Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: 3d-r2n2: a unified approach for single and multi-view 3d object reconstruction. In: European Conference on Computer Vision, pp. 628\u2013644. Springer, Heidelberg (2016)","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Dai, Y., Zhu, Z., Rao, Z., Li, B.: Mvs2: deep unsupervised multi-view stereo with multi-view symmetry. In: 2019 International Conference on 3D Vision (3DV), pp.\u00a01\u20138. IEEE (2019)","DOI":"10.1109\/3DV.2019.00010"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Ding, Y., et al.: Transmvsnet: global context-aware multi-view stereo network with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8585\u20138594 (2022)","DOI":"10.1109\/CVPR52688.2022.00839"},{"issue":"8","key":"9_CR12","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","volume":"32","author":"Y Furukawa","year":"2009","unstructured":"Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362\u20131376 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR13","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":"9_CR14","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac\u00a0Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 270\u2013279 (2017)","DOI":"10.1109\/CVPR.2017.699"},{"key":"9_CR15","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":"9_CR16","doi-asserted-by":"crossref","unstructured":"Huang, B., Yi, H., Huang, C., He, Y., Liu, J., Liu, X.: M3vsnet: unsupervised multi-metric multi-view stereo network. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 3163\u20133167. IEEE (2021)","DOI":"10.1109\/ICIP42928.2021.9506469"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L.: Surfacenet: an end-to-end 3d neural network for multiview stereopsis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2307\u20132315 (2017)","DOI":"10.1109\/ICCV.2017.253"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Karaboga, D., Basturk, B.: Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: International Fuzzy Systems Association World Congress, pp. 789\u2013798. Springer, Heidelberg (2007)","DOI":"10.1007\/978-3-540-72950-1_77"},{"issue":"3","key":"9_CR19","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. (ToG) 32(3), 1\u201313 (2013)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"9_CR20","unstructured":"Khot, T., Agrawal, S., Tulsiani, S., Mertz, C., Lucey, S., Hebert, M.: Learning unsupervised multi-view stereopsis via robust photometric consistency. arXiv preprint arXiv:1905.02706 (2019)"},{"issue":"4","key":"9_CR21","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)"},{"issue":"3","key":"9_CR22","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/A:1008191222954","volume":"38","author":"KN Kutulakos","year":"2000","unstructured":"Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. Int. J. Comput. Vision 38(3), 199\u2013218 (2000)","journal-title":"Int. J. Comput. Vision"},{"issue":"3","key":"9_CR23","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/TPAMI.2005.44","volume":"27","author":"M Lhuillier","year":"2005","unstructured":"Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 418\u2013433 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Liao, J., Fu, Y., Yan, Q., Xiao, C.: Pyramid multi-view stereo with local consistency. In: Computer Graphics Forum, vol.\u00a038, pp. 335\u2013346. Wiley Online Library (2019)","DOI":"10.1111\/cgf.13841"},{"issue":"10","key":"9_CR25","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1109\/TPAMI.2015.2505283","volume":"38","author":"F Liu","year":"2015","unstructured":"Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2024\u20132039 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR26","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 (ICCV), pp. 18088\u201318097 (2023)","DOI":"10.1109\/ICCV51070.2023.01658"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Luo, K., Guan, T., Ju, L., Huang, H., Luo, Y.: P-mvsnet: learning patch-wise matching confidence aggregation for multi-view stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10452\u201310461 (2019)","DOI":"10.1109\/ICCV.2019.01055"},{"key":"9_CR28","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":"9_CR29","doi-asserted-by":"crossref","unstructured":"Ma, X., Gong, Y., Wang, Q., Huang, J., Chen, L., Yu, F.: Epp-mvsnet: epipolar-assembling based depth prediction for multi-view stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5732\u20135740 (2021)","DOI":"10.1109\/ICCV48922.2021.00568"},{"key":"9_CR30","unstructured":"Mallick, A., St\u00fcckler, J., Lensch, H.: Learning to adapt multi-view stereo by self-supervision. arXiv preprint arXiv:2009.13278 (2020)"},{"issue":"5","key":"9_CR31","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1007\/s00371-021-02087-5","volume":"38","author":"W Mao","year":"2022","unstructured":"Mao, W., Wang, M., Huang, H., Gong, M.: A robust framework for multi-view stereopsis. Vis. Comput. 38(5), 1539\u20131551 (2022)","journal-title":"Vis. Comput."},{"issue":"2","key":"9_CR32","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1214\/aoms\/1177692644","volume":"43","author":"G Marsaglia","year":"1972","unstructured":"Marsaglia, G.: Choosing a point from the surface of a sphere. Ann. Math. Stat. 43(2), 645\u2013646 (1972)","journal-title":"Ann. Math. Stat."},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3d reconstruction in function space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Mi, Z., Di, C., Xu, D.: Generalized binary search network for highly-efficient multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12991\u201313000 (2022)","DOI":"10.1109\/CVPR52688.2022.01265"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Peng, R., Wang, R., Wang, Z., Lai, Y., Wang, R.: Rethinking depth estimation for multi-view stereo: a unified representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8645\u20138654 (2022)","DOI":"10.1109\/CVPR52688.2022.00845"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Romanoni, A., Matteucci, M.: Tapa-mvs: textureless-aware patchmatch multi-view stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10413\u201310422 (2019)","DOI":"10.1109\/ICCV.2019.01051"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Frahm, J.M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: European Conference on Computer Vision, pp. 501\u2013518. Springer, Heidelberg (2016)","DOI":"10.1007\/978-3-319-46487-9_31"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 06), vol.\u00a01, pp. 519\u2013528. IEEE (2006)","DOI":"10.1109\/CVPR.2006.19"},{"issue":"2","key":"9_CR39","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1023\/A:1008176507526","volume":"35","author":"SM Seitz","year":"1999","unstructured":"Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. Int. J. Comput. Vision 35(2), 151\u2013173 (1999)","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Tang, J., Han, X., Pan, J., Jia, K., Tong, X.: A skeleton-bridged deep learning approach for generating meshes of complex topologies from single rgb images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4541\u20134550 (2019)","DOI":"10.1109\/CVPR.2019.00467"},{"key":"9_CR41","doi-asserted-by":"crossref","unstructured":"Tang, J., Han, X., Tan, M., Tong, X., Jia, K.: Skeletonnet: a topology-preserving solution for learning mesh reconstruction of object surfaces from rgb images. IEEE Trans. Pattern Anal. Mach. Intell. (2021)","DOI":"10.1109\/TPAMI.2021.3087358"},{"key":"9_CR42","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: efficient convolutional architectures for high-resolution 3d outputs. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2088\u20132096 (2017)","DOI":"10.1109\/ICCV.2017.230"},{"key":"9_CR43","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: Kpconv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6411\u20136420 (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"issue":"5","key":"9_CR44","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1109\/TPAMI.2009.77","volume":"32","author":"E Tola","year":"2009","unstructured":"Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815\u2013830 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Wang, F., Galliani, S., Vogel, C., Speciale, P., Pollefeys, M.: Patchmatchnet: learned multi-view patchmatch stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14194\u201314203 (2021)","DOI":"10.1109\/CVPR46437.2021.01397"},{"key":"9_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102102","volume":"70","author":"X Wang","year":"2021","unstructured":"Wang, X., et al.: Multi-view stereo in the deep learning era: a comprehensive review. Displays 70, 102102 (2021)","journal-title":"Displays"},{"key":"9_CR47","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Adaptive patch deformation for textureless-resilient multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1621\u20131630 (2023)","DOI":"10.1109\/CVPR52729.2023.00162"},{"key":"9_CR48","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qian, Y., Li, Y., Gong, M., Banzhaf, W.: Artificial multi-bee-colony algorithm for k-nearest-neighbor fields search. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 1037\u20131044 (2016)","DOI":"10.1145\/2908812.2908835"},{"issue":"10","key":"9_CR49","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1109\/TPAMI.2017.2754254","volume":"40","author":"S Wu","year":"2017","unstructured":"Wu, S., Bertholet, P., Huang, H., Cohen-Or, D., Gong, M., Zwicker, M.: Structure-aware data consolidation. IEEE Trans. Pattern Anal. Mach. Intell. 40(10), 2529\u20132537 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR50","doi-asserted-by":"crossref","unstructured":"Xu, Q., Kong, W., Tao, W., Pollefeys, M.: Multi-scale geometric consistency guided and planar prior assisted multi-view stereo. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3200074"},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Xu, Q., Tao, W.: Multi-scale geometric consistency guided multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5483\u20135492 (2019)","DOI":"10.1109\/CVPR.2019.00563"},{"key":"9_CR52","doi-asserted-by":"crossref","unstructured":"Xu, Q., Tao, W.: Planar prior assisted patchmatch multi-view stereo. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 12516\u201312523 (2020)","DOI":"10.1609\/aaai.v34i07.6940"},{"key":"9_CR53","doi-asserted-by":"crossref","unstructured":"Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4541\u20134550 (2019)","DOI":"10.1109\/ICCV.2019.00464"},{"key":"9_CR54","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":"9_CR55","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":"9_CR56","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":"9_CR57","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":"9_CR58","doi-asserted-by":"crossref","unstructured":"Yin, Z., Shi, J.: Geonet: unsupervised learning of dense depth, optical flow and camera pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1983\u20131992 (2018)","DOI":"10.1109\/CVPR.2018.00212"},{"issue":"1","key":"9_CR59","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. Vision 131(1), 199\u2013214 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR60","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"},{"key":"9_CR61","doi-asserted-by":"publisher","unstructured":"Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166\u20133173 (2010). https:\/\/doi.org\/10.1016\/j.amc.2010.08.049. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0096300310009136","DOI":"10.1016\/j.amc.2010.08.049"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78456-9_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T12:10:13Z","timestamp":1733141413000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78456-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"ISBN":["9783031784552","9783031784569"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78456-9_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"3 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}