{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:14:03Z","timestamp":1743020043232,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819756148"},{"type":"electronic","value":"9789819756155"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5615-5_10","type":"book-chapter","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T13:12:02Z","timestamp":1722604322000},"page":"118-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IMFA-Stereo: Domain Generalized Stereo Matching via Iterative Multimodal Feature Aggregation Cost Volume"],"prefix":"10.1007","author":[{"given":"Gang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jinlong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,3]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040\u20134048 (2016)","DOI":"10.1109\/CVPR.2016.438"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Liu, B., Yu, H., Long, Y.: Local similarity pattern and cost self-reassembling for deep stereo matching networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1647\u20131655 (2022)","DOI":"10.1609\/aaai.v36i2.20056"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, F., Prisacariu, V., Yang, R., Torr, P.H.: GA-Net: guided aggregation net for end-to-end stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 185\u2013194 (2019)","DOI":"10.1109\/CVPR.2019.00027"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, J.: AANet: adaptive aggregation network for efficient stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1959\u20131968 (2020)","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361 (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3061\u20133070 (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","volume":"30","author":"H Hirschmuller","year":"2007","unstructured":"Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328\u2013341 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Proceedings of the German Conference on Pattern Recognition, pp. 31\u201342 (2014)","DOI":"10.1007\/978-3-319-11752-2_3"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Schops, T., et al.: A multi-view stereo benchmark with high-resolution images and multi-camera videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3260\u20133269 (2017)","DOI":"10.1109\/CVPR.2017.272"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., Deng, J.: Raft-stereo: Multilevel recurrent field transforms for stereo matching. In: Proceedings of the International Conference on 3D Vision, pp. 218\u2013227 (2021)","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Xu, G., Wang, X., Ding, X., Yang, X.: Iterative geometry encoding volume for stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21919\u201321928 (2023)","DOI":"10.1109\/CVPR52729.2023.02099"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv preprint arXiv: 1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"issue":"11","key":"10_CR14","doi-asserted-by":"publisher","first-page":"13941","DOI":"10.1109\/TPAMI.2023.3298645","volume":"45","author":"H Xu","year":"2023","unstructured":"Xu, H., et al.: Unifying flow, stereo and depth estimation. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 13941\u201313958 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Wu, Z., Wu, X., Zhang, X., Wang, S., Ju, L.: Semantic stereo matching with pyramid cost volumes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7484\u20137493 (2019)","DOI":"10.1109\/ICCV.2019.00758"},{"key":"10_CR16","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":"10_CR17","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1007\/s11263-019-01287-w","volume":"128","author":"X Song","year":"2020","unstructured":"Song, X., Zhao, X., Fang, L., Hu, H., Yu, Y.: EdgeStereo: an effective multi-task learning network for stereo matching and edge detection. Int. J. Comput. Vision 128, 910\u2013930 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, Y., Bai, X., Yu, S., Yu, K., Li, Z., Yang, K.: Adaptive unimodal cost volume filtering for deep stereo matching. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12926\u201312934 (2020)","DOI":"10.1609\/aaai.v34i07.6991"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Zeng, J., Yao, C., Yu, L., Wu, Y., Jia, Y.: Parameterized cost volume for stereo matching. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 18347\u201318357 (2023)","DOI":"10.1109\/ICCV51070.2023.01682"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Shamsafar, F., Woerz, S., Rahim, R., Zell, A.: MobileStereoNet: towards lightweight deep networks for stereo matching. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2417\u20132426 (2022)","DOI":"10.1109\/WACV51458.2022.00075"},{"issue":"12","key":"10_CR22","doi-asserted-by":"publisher","first-page":"14301","DOI":"10.1109\/TPAMI.2023.3300976","volume":"45","author":"Z Shen","year":"2023","unstructured":"Shen, Z., Song, X., Dai, Y., Zhou, D., Rao, Z., Zhang, L.: Digging into uncertainty-based pseudo-label for robust stereo matching. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 14301\u201314320 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Shen, Z., Dai, Y., Rao, Z.: CFNet: cascade and fused cost volume for robust stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13906\u201313915 (2021)","DOI":"10.1109\/CVPR46437.2021.01369"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6197\u20136206 (2021)","DOI":"10.1109\/ICCV48922.2021.00614"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Rao, Z., et al.: Masked representation learning for domain generalized stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5435\u20135444 (2023)","DOI":"10.1109\/CVPR52729.2023.00526"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5615-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T11:03:41Z","timestamp":1731150221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5615-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756148","9789819756155"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5615-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}