{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T16:56:42Z","timestamp":1786553802455,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585914","type":"print"},{"value":"9783030585921","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58592-1_40","type":"book-chapter","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:34:03Z","timestamp":1604363643000},"page":"678-693","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["PointAR: Efficient Lighting Estimation for Mobile Augmented Reality"],"prefix":"10.1007","author":[{"given":"Yiqin","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"40_CR1","unstructured":"ARCore. https:\/\/developers.google.com\/ar. Accessed 3 Mar 2020"},{"key":"40_CR2","unstructured":"TensorFlow Mobile and IoT Hosted Models. https:\/\/www.tensorflow.org\/lite\/guide\/hosted_models"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Apicharttrisorn, K., Ran, X., Chen, J., Krishnamurthy, S.V., Roy-Chowdhury, A.K.: Frugal following: power thrifty object detection and tracking for mobile augmented reality. In: Proceedings of the 17th Conference on Embedded Networked Sensor Systems, SenSys 2019, pp. 96\u2013109. ACM, New York (2019)","DOI":"10.1145\/3356250.3360044"},{"key":"40_CR4","unstructured":"Apple: adding realistic reflections to an AR experience. https:\/\/developer.apple.com\/documentation\/arkit\/adding_realistic_reflections_to_an_ar_experience. Accessed 10 July 2020"},{"key":"40_CR5","unstructured":"Apple Inc: Augmented reality - apple developer. https:\/\/developer.apple.com\/augmented-reality\/. Accessed 3 Mar 2020"},{"key":"40_CR6","unstructured":"Armeni, I., Sax, A., Zamir, A.R., Savarese, S.: Joint 2D\u20133D-semantic data for indoor scene understanding. ArXiv e-prints, February 2017"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Chang, A., et al.: Matterport3D: learning from RGB-D data in indoor environments. arXiv preprint arXiv:1709.06158 (2017)","DOI":"10.1109\/3DV.2017.00081"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Cheng, D., Shi, J., Chen, Y., Deng, X., Zhang, X.: Learning scene illumination by pairwise photos from rear and front mobile cameras. Comput. Graph. Forum 37(7), 213\u2013221 (2018). http:\/\/dblp.uni-trier.de\/db\/journals\/cgf\/cgf37.html#ChengSCDZ18","DOI":"10.1111\/cgf.13561"},{"key":"40_CR9","unstructured":"Chuang, Y.Y.: Camera calibration (2005)"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Debevec, P.: Image-based lighting. In: ACM SIGGRAPH 2006 Courses, pp. 4-es (2006)","DOI":"10.1145\/1185657.1185686"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Gardner, M.A., Hold-Geoffroy, Y., Sunkavalli, K., Gagne, C., Lalonde, J.F.: Deep parametric indoor lighting estimation. In: The IEEE International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00727"},{"issue":"6","key":"40_CR12","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/3130800.3130891","volume":"36","author":"M Gardner","year":"2017","unstructured":"Gardner, M., et al.: Learning to predict indoor illumination from a single image. ACM Trans. Graph. 36(6), 14 (2017). https:\/\/doi.org\/10.1145\/3130800.3130891. Article No. 176","journal-title":"ACM Trans. Graph."},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Garon, M., Sunkavalli, K., Hadap, S., Carr, N., Lalonde, J.: Fast spatially-varying indoor lighting estimation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00707"},{"key":"40_CR14","unstructured":"Google. https:\/\/developers.google.com\/ar\/develop\/unity\/light-estimation\/developer-guide-unity"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Gruber, L., Richter-Trummer, T., Schmalstieg, D.: Real-time photometric registration from arbitrary geometry. In: 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 119\u2013128. IEEE (2012)","DOI":"10.1109\/ISMAR.2012.6402548"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR17","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). http:\/\/arxiv.org\/abs\/1704.04861, cite arxiv:1704.04861"},{"key":"40_CR18","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$0.5mb model size (2016). http:\/\/arxiv.org\/abs\/1602.07360, cite arxiv:1602.07360Comment. In ICLR Format"},{"key":"40_CR19","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on x-transformed points. In: Advances in Neural Information Processing Systems, pp. 820\u2013830 (2018)"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Liu, L., Li, H., Gruteser, M.: Edge assisted real-time object detection for mobile augmented reality. In: The 25th Annual International Conference on Mobile Computing and Networking (MobiCom 2019) (2019)","DOI":"10.1145\/3300061.3300116"},{"issue":"19","key":"40_CR21","doi-asserted-by":"publisher","first-page":"4188","DOI":"10.3390\/s19194188","volume":"19","author":"W Liu","year":"2019","unstructured":"Liu, W., Sun, J., Li, W., Hu, T., Wang, P.: Deep learning on point clouds and its application: a survey. Sensors 19(19), 4188 (2019)","journal-title":"Sensors"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Prakash, S., Bahremand, A., Nguyen, L.D., LiKamWa, R.: GLEAM: an illumination estimation framework for real-time photorealistic augmented reality on mobile devices. In: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2019, pp. 142\u2013154. Association for Computing Machinery, New York, June 2019","DOI":"10.1145\/3307334.3326098"},{"key":"40_CR23","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. arXiv preprint arXiv:1612.00593 (2016)"},{"key":"40_CR24","doi-asserted-by":"publisher","unstructured":"Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environment maps. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 2001, pp. 497\u2013500. ACM Press, Not Known (2001). https:\/\/doi.org\/10.1145\/383259.383317. http:\/\/portal.acm.org\/citation.cfm?doid=383259.383317","DOI":"10.1145\/383259.383317"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Song, S., Funkhouser, T.: Neural illumination: lighting prediction for indoor environments. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00708"},{"key":"40_CR26","unstructured":"Sze, V., Chen, Y., Yang, T., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. CoRR (2017). http:\/\/arxiv.org\/abs\/1703.09039"},{"key":"40_CR27","unstructured":"Tulloch, A., et al.: Enabling full body AR with mask R-CNN2Go - facebook research, January 2018. https:\/\/research.fb.com\/blog\/2018\/01\/enabling-full-body-ar-with-mask-r-cnn2go\/. Accessed 3 Mar 2020"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00985"},{"key":"40_CR29","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912\u20131920 (2015)"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: SpiderCNN: deep learning on point sets with parameterized convolutional filters. arXiv preprint arXiv:1803.11527 (2018)","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"40_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, E., Cohen, M.F., Curless, B.: Discovering point lights with intensity distance fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6635\u20136643 (2018)","DOI":"10.1109\/CVPR.2018.00694"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58592-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:32:04Z","timestamp":1730593924000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58592-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585914","9783030585921"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58592-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}