{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:35:31Z","timestamp":1778258131095,"version":"3.51.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012304","type":"print"},{"value":"9783030012311","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01231-1_20","type":"book-chapter","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T16:03:25Z","timestamp":1538755405000},"page":"323-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Beyond Local Reasoning for Stereo Confidence Estimation with Deep Learning"],"prefix":"10.1007","author":[{"given":"Fabio","family":"Tosi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Poggi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Benincasa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Mattoccia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Poggi, M., Tosi, F., Mattoccia, S.: Quantitative evaluation of confidence measures in a machine learning world. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.559"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Poggi, M., Mattoccia, S.: Learning from scratch a confidence measure. In: Proceedings of the 27th British Conference on Machine Vision, BMVC (2016)","DOI":"10.5244\/C.30.46"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Seki, A., Pollefeys, M.: Patch based confidence prediction for dense disparity map. In: British Machine Vision Conference (BMVC) (2016)","DOI":"10.5244\/C.30.23"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Poggi, M., Mattoccia, S.: Learning a general-purpose confidence measure based on o(1) features and a smarter aggregation strategy for semi global matching. In: Proceedings of the 4th International Conference on 3D Vision, 3DV (2016)","DOI":"10.1109\/3DV.2016.61"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Haeusler, R., Nair, R., Kondermann, D.: Ensemble learning for confidence measures in stereo vision. In: CVPR Proceedings, pp. 305\u2013312 (2013)","DOI":"10.1109\/CVPR.2013.46"},{"key":"20_CR6","unstructured":"Fu, Z., Ardabilian, M.: Stereo matching confidence learning based on multi-modal convolution neural networks. In: Representation, Analysis and Recognition of Shape and Motion from Image Data (RFMI) (2017)"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"issue":"1\u201332","key":"20_CR8","first-page":"2","volume":"17","author":"J Zbontar","year":"2016","unstructured":"Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(1\u201332), 2 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"20_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-319-11752-2_3","volume-title":"Pattern Recognition","author":"D Scharstein","year":"2014","unstructured":"Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31\u201342. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11752-2_3"},{"key":"20_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/BFb0028345","volume-title":"Computer Vision \u2014 ECCV \u201994","author":"R Zabih","year":"1994","unstructured":"Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151\u2013158. Springer, Heidelberg (1994). https:\/\/doi.org\/10.1007\/BFb0028345"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Hirschmuller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 807\u2013814. IEEE (2005)","DOI":"10.1109\/CVPR.2005.56"},{"key":"20_CR13","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1109\/TPAMI.2011.283","volume":"34","author":"X Hu","year":"2012","unstructured":"Hu, X., Mordohai, P.: A quantitative evaluation of confidence measures for stereo vision. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 34, 2121\u20132133 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"issue":"1\u20133","key":"20_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2002","unstructured":"Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47(1\u20133), 7\u201342 (2002)","journal-title":"Int. J. Comput. Vision"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Spyropoulos, A., Komodakis, N., Mordohai, P.: Learning to detect ground control points for improving the accuracy of stereo matching. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1621\u20131628. IEEE (2014)","DOI":"10.1109\/CVPR.2014.210"},{"key":"20_CR16","unstructured":"Park, M.G., Yoon, K.J.: Leveraging stereo matching with learning-based confidence measures. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Poggi, M., Mattoccia, S.: Learning to predict stereo reliability enforcing local consistency of confidence maps. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.483"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Poggi, M., Tosi, F., Mattoccia, S.: Even more confident predictions with deep machine-learning. In: 12th IEEE Embedded Vision Workshop (EVW2017) Held in Conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPRW.2017.54"},{"issue":"12","key":"20_CR19","doi-asserted-by":"publisher","first-page":"6019","DOI":"10.1109\/TIP.2017.2750404","volume":"26","author":"S Kim","year":"2017","unstructured":"Kim, S., Min, D., Kim, S., Sohn, K.: Feature augmentation for learning confidence measure in stereo matching. IEEE Trans. Image Process. 26(12), 6019\u20136033 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Mostegel, C., Rumpler, M., Fraundorfer, F., Bischof, H.: Using self-contradiction to learn confidence measures in stereo vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 4067\u20134076 (2016)","DOI":"10.1109\/CVPR.2016.441"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Tosi, F., Poggi, M., Tonioni, A., Di Stefano, L., Mattoccia, S.: Learning confidence measures in the wild. In: 28th British Machine Vision Conference, BMVC 2017, September 2017","DOI":"10.5244\/C.31.133"},{"key":"20_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-68560-1_43","volume-title":"Image Analysis and Processing - ICIAP 2017","author":"M Poggi","year":"2017","unstructured":"Poggi, M., Tosi, F., Mattoccia, S.: Efficient confidence measures for embedded stereo. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 483\u2013494. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68560-1_43"},{"key":"20_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-319-46478-7_24","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Marin","year":"2016","unstructured":"Marin, G., Zanuttigh, P., Mattoccia, S.: Reliable fusion of TOF and stereo depth driven by confidence measures. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 386\u2013401. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_24"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Poggi, M., Mattoccia, S.: Deep stereo fusion: combining multiple disparity hypotheses with deep-learning. In: Proceedings of the 4th International Conference on 3D Vision, 3DV (2016)","DOI":"10.1109\/3DV.2016.22"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Shaked, A., Wolf, L.: Improved stereo matching with constant highway networks and reflective confidence learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.730"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Tonioni, A., Poggi, M., Mattoccia, S., Di Stefano, L.: Unsupervised adaptation for deep stereo. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.178"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Zbontar, J., LeCun, Y.: Computing the stereo matching cost with a convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1592\u20131599 (2015)","DOI":"10.1109\/CVPR.2015.7298767"},{"key":"20_CR28","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: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.438"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCV.2017.17"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Pang, J., Sun, W., Ren, J.S., Yang, C., Yan, Q.: Cascade residual learning: a two-stage convolutional neural network for stereo matching. In: The IEEE International Conference on Computer Vision (ICCV), October 2017","DOI":"10.1109\/ICCVW.2017.108"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01231-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:36:14Z","timestamp":1775241374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01231-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012304","9783030012311"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01231-1_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}