{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:41:50Z","timestamp":1742913710508,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985364"},{"type":"electronic","value":"9789819985371"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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\/978-981-99-8537-1_2","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"15-27","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ContextNet: Learning Context Information for\u00a0Texture-Less Light Field Depth Estimation"],"prefix":"10.1007","author":[{"given":"Wentao","family":"Chao","sequence":"first","affiliation":[]},{"given":"Xuechun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yiming","family":"Kan","sequence":"additional","affiliation":[]},{"given":"Fuqing","family":"Duan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Chao, W., Duan, F., Wang, X., Wang, Y., Wang, G.: Occcasnet: occlusion-aware cascade cost volume for light field depth estimation. arXiv preprint arXiv:2305.17710 (2023)","DOI":"10.1109\/TCI.2024.3488563"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Chao, W., Wang, X., Wang, Y., Wang, G., Duan, F.: Learning sub-pixel disparity distribution for light field depth estimation. TCI Early Access, 1\u201312 (2023)","DOI":"10.1109\/TCI.2023.3336184"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, S., Lin, Y.: Attention-based multi-level fusion network for light field depth estimation. In: AAAI, pp. 1009\u20131017 (2021)","DOI":"10.1609\/aaai.v35i2.16185"},{"issue":"4","key":"2_CR4","first-page":"855","volume":"27","author":"J Chen","year":"2017","unstructured":"Chen, J., Chau, L.: Light field compressed sensing over a disparity-aware dictionary. TCSVT 27(4), 855\u2013865 (2017)","journal-title":"TCSVT"},{"key":"2_CR5","first-page":"397","volume":"8","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Zhang, S., Chang, S., Lin, Y.: Light field reconstruction using efficient pseudo 4d epipolar-aware structure. TCI 8, 397\u2013410 (2022)","journal-title":"TCI"},{"key":"2_CR6","first-page":"1131","volume":"8","author":"Z Cheng","year":"2022","unstructured":"Cheng, Z., Liu, Y., Xiong, Z.: Spatial-angular versatile convolution for light field reconstruction. TCI 8, 1131\u20131144 (2022)","journal-title":"TCI"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Xiong, Z., Chen, C., Liu, D., Zha, Z.J.: Light field super-resolution with zero-shot learning. In: CVPR, pp. 10010\u201310019 (2021)","DOI":"10.1109\/CVPR46437.2021.00988"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Guo, C., Jin, J., Hou, J., Chen, J.: Accurate light field depth estimation via an occlusion-aware network. In: ICME, pp. 1\u20136 (2020)","DOI":"10.1109\/ICME46284.2020.9102829"},{"issue":"11","key":"2_CR9","first-page":"8022","volume":"44","author":"K Han","year":"2022","unstructured":"Han, K., Xiang, W., Wang, E., Huang, T.: A novel occlusion-aware vote cost for light field depth estimation. TPAMI 44(11), 8022\u20138035 (2022)","journal-title":"TPAMI"},{"issue":"9","key":"2_CR10","first-page":"4676","volume":"27","author":"L He","year":"2018","unstructured":"He, L., Wang, G., Hu, Z.: Learning depth from single images with deep neural network embedding focal length. TIP 27(9), 4676\u20134689 (2018)","journal-title":"TIP"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Heber, S., Pock, T.: Convolutional networks for shape from light field. In: CVPR, pp. 3746\u20133754 (2016)","DOI":"10.1109\/CVPR.2016.407"},{"key":"2_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-54187-7_2","volume-title":"Computer Vision \u2013 ACCV 2016","author":"K Honauer","year":"2017","unstructured":"Honauer, K., Johannsen, O., Kondermann, D., Goldluecke, B.: A dataset and evaluation methodology for depth estimation on 4D\u00a0light fields. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10113, pp. 19\u201334. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54187-7_2"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Jeon, H.G., et al.: Accurate depth map estimation from a lenslet light field camera. In: CVPR, pp. 1547\u20131555 (2015)","DOI":"10.1109\/CVPR.2015.7298762"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Jin, J., Hou, J., Chen, J., Kwong, S.: Light field spatial super-resolution via deep combinatorial geometry embedding and structural consistency regularization. In: CVPR, pp. 2260\u20132269 (2020)","DOI":"10.1109\/CVPR42600.2020.00233"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TPAMI.2020.3026039","volume":"44","author":"J Jin","year":"2020","unstructured":"Jin, J., Hou, J., Chen, J., Zeng, H., Kwong, S., Yu, J.: Deep coarse-to-fine dense light field reconstruction with flexible sampling and geometry-aware fusion. TPAMI 44, 1819\u20131836 (2020)","journal-title":"TPAMI"},{"issue":"9","key":"2_CR16","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. TPAMI 37(9), 1904\u20131916 (2015)","journal-title":"TPAMI"},{"issue":"4","key":"2_CR17","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1145\/2461912.2461926","volume":"32","author":"C Kim","year":"2013","unstructured":"Kim, C., Zimmer, H., Pritch, Y., Sorkine-Hornung, A., Gross, M.H.: Scene reconstruction from high spatio-angular resolution light fields. TOG 32(4), 73\u20131 (2013)","journal-title":"TOG"},{"key":"2_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"3","key":"2_CR19","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1109\/TPAMI.2019.2945027","volume":"43","author":"N Meng","year":"2019","unstructured":"Meng, N., So, H.K.H., Sun, X., Lam, E.Y.: High-dimensional dense residual convolutional neural network for light field reconstruction. TPAMI 43(3), 873\u2013886 (2019)","journal-title":"TPAMI"},{"key":"2_CR20","unstructured":"Ng, R., Levoy, M., Br\u00e9dif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Ph.D. thesis, Stanford University (2005)"},{"key":"2_CR21","first-page":"682","volume":"6","author":"J Peng","year":"2020","unstructured":"Peng, J., Xiong, Z., Wang, Y., Zhang, Y., Liu, D.: Zero-shot depth estimation from light field using a convolutional neural network. TCI 6, 682\u2013696 (2020)","journal-title":"TCI"},{"issue":"11","key":"2_CR22","first-page":"7880","volume":"32","author":"H Sheng","year":"2022","unstructured":"Sheng, H., Cong, R., Yang, D., Chen, R., Wang, S., Cui, Z.: Urbanlf: a comprehensive light field dataset for semantic segmentation of urban scenes. TCSVT 32(11), 7880\u20137893 (2022)","journal-title":"TCSVT"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Shin, C., Jeon, H.G., Yoon, Y., Kweon, I.S., Kim, S.J.: Epinet: a fully-convolutional neural network using epipolar geometry for depth from light field images. In: CVPR, pp. 4748\u20134757 (2018)","DOI":"10.1109\/CVPR.2018.00499"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Tao, M.W., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: ICCV, pp. 673\u2013680 (2013)","DOI":"10.1109\/ICCV.2013.89"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Tsai, Y.J., Liu, Y.L., Ouhyoung, M., Chuang, Y.Y.: Attention-based view selection networks for light-field disparity estimation. In: AAAI, pp. 12095\u201312103 (2020)","DOI":"10.1609\/aaai.v34i07.6888"},{"key":"2_CR26","first-page":"350","volume":"9","author":"V Van Duong","year":"2023","unstructured":"Van Duong, V., Huu, T.N., Yim, J., Jeon, B.: Light field image super-resolution network via joint spatial-angular and epipolar information. TCI 9, 350\u2013366 (2023)","journal-title":"TCI"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, L., Liang, Z., Yang, J., An, W., Guo, Y.: Occlusion-aware cost constructor for light field depth estimation. In: CVPR, pp. 19809\u201319818 (2022)","DOI":"10.1109\/CVPR52688.2022.01919"},{"key":"2_CR28","unstructured":"Wang, Y., Wang, L., Liang, Z., Yang, J., Timofte, R., Guo, Y.: Ntire 2023 challenge on light field image super-resolution: dataset, methods and results. arXiv preprint arXiv:2304.10415 (2023)"},{"key":"2_CR29","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TPAMI.2022.3152488","volume":"45","author":"Y Wang","year":"2022","unstructured":"Wang, Y., et al.: Disentangling light fields for super-resolution and disparity estimation. TPAMI 45, 425\u2013443 (2022)","journal-title":"TPAMI"},{"issue":"1","key":"2_CR30","first-page":"204","volume":"26","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Yang, J., Guo, Y., Xiao, C., An, W.: Selective light field refocusing for camera arrays using bokeh rendering and superresolution. SPL 26(1), 204\u2013208 (2018)","journal-title":"SPL"},{"issue":"3","key":"2_CR31","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TPAMI.2013.147","volume":"36","author":"S Wanner","year":"2014","unstructured":"Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. TPAMI 36(3), 606\u2013619 (2014)","journal-title":"TPAMI"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Williem, W., Park, I.K.: Robust light field depth estimation for noisy scene with occlusion. In: CVPR, pp. 4396\u20134404 (2016)","DOI":"10.1109\/CVPR.2016.476"},{"issue":"7","key":"2_CR33","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/TPAMI.2018.2845393","volume":"41","author":"G Wu","year":"2018","unstructured":"Wu, G., Liu, Y., Fang, L., Dai, Q., Chai, T.: Light field reconstruction using convolutional network on epi and extended applications. TPAMI 41(7), 1681\u20131694 (2018)","journal-title":"TPAMI"},{"key":"2_CR34","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"issue":"2","key":"2_CR35","first-page":"104","volume":"24","author":"J Yu","year":"2017","unstructured":"Yu, J.: A light-field journey to virtual reality. TMM 24(2), 104\u2013112 (2017)","journal-title":"TMM"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, S., Lin, Y., Sheng, H.: Residual networks for light field image super-resolution. In: CVPR, pp. 11046\u201311055 (2019)","DOI":"10.1109\/CVPR.2019.01130"},{"key":"2_CR37","first-page":"148","volume":"145","author":"S Zhang","year":"2016","unstructured":"Zhang, S., Sheng, H., Li, C., Zhang, J., Xiong, Z.: Robust depth estimation for light field via spinning parallelogram operator. CVIU 145, 148\u2013159 (2016)","journal-title":"CVIU"},{"issue":"4","key":"2_CR38","first-page":"739","volume":"27","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., et al.: Light-field depth estimation via epipolar plane image analysis and locally linear embedding. TCSVT 27(4), 739\u2013747 (2016)","journal-title":"TCSVT"},{"issue":"11","key":"2_CR39","first-page":"4269","volume":"30","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Dai, W., Xu, M., Zou, J., Zhang, X., Xiong, H.: Depth estimation from light field using graph-based structure-aware analysis. TCSVT 30(11), 4269\u20134283 (2019)","journal-title":"TCSVT"},{"issue":"7","key":"2_CR40","first-page":"965","volume":"11","author":"H Zhu","year":"2017","unstructured":"Zhu, H., Wang, Q., Yu, J.: Occlusion-model guided antiocclusion depth estimation in light field. J-STSP 11(7), 965\u2013978 (2017)","journal-title":"J-STSP"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8537-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T20:48:49Z","timestamp":1730926129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8537-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819985364","9789819985371"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8537-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}