{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T14:06:19Z","timestamp":1782828379615,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#1755659 and #1815619"],"award-info":[{"award-number":["#1755659 and #1815619"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,24]]},"DOI":"10.1145\/3458864.3467886","type":"proceedings-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T16:13:50Z","timestamp":1624378430000},"page":"28-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Xihe"],"prefix":"10.1145","author":[{"given":"Yiqin","family":"Zhao","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Guo","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. Depth buffer - The gritty details.  [n.d.]. Depth buffer - The gritty details."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360044"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274783.3274834"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809711"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13561"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1185657.1185686"},{"key":"e_1_3_2_1_7_1","volume-title":"Learning to Predict Indoor Illumination from a Single Image. ACM Transactions on Graphics","author":"Gardner Marc-Andr\u00e9","year":"2017","unstructured":"Marc-Andr\u00e9 Gardner , Kalyan Sunkavalli , Ersin Yumer , Xiaohui Shen , Emiliano Gambaretto , Christian Gagn\u00e9 , and Jean-Fran\u00e7ois Lalonde . 2017. Learning to Predict Indoor Illumination from a Single Image. ACM Transactions on Graphics ( 2017 ). Marc-Andr\u00e9 Gardner, Kalyan Sunkavalli, Ersin Yumer, Xiaohui Shen, Emiliano Gambaretto, Christian Gagn\u00e9, and Jean-Fran\u00e7ois Lalonde. 2017. Learning to Predict Indoor Illumination from a Single Image. ACM Transactions on Graphics (2017)."},{"key":"e_1_3_2_1_8_1","volume-title":"Fast Spatially-Varying Indoor Lighting Estimation. CVPR","author":"Garon Mathieu","year":"2019","unstructured":"Mathieu Garon , Kalyan Sunkavalli , Sunil Hadap , Nathan Carr , and Jean-Fran\u00e7ois Lalonde . 2019. Fast Spatially-Varying Indoor Lighting Estimation. CVPR ( 2019 ). Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, and Jean-Fran\u00e7ois Lalonde. 2019. Fast Spatially-Varying Indoor Lighting Estimation. CVPR (2019)."},{"key":"e_1_3_2_1_9_1","unstructured":"Google. 2020. ARCore. https:\/\/developers.google.com\/ar.  Google. 2020. ARCore. https:\/\/developers.google.com\/ar."},{"key":"e_1_3_2_1_10_1","unstructured":"Google for Education. [n.d.]. Bringing virtual and augmented reality to school | Google for Education. https:\/\/edu.google.com\/products\/vr-ar\/?modal_active=none. Accessed: 2020-7-24.  Google for Education. [n.d.]. Bringing virtual and augmented reality to school | Google for Education. https:\/\/edu.google.com\/products\/vr-ar\/?modal_active=none. Accessed: 2020-7-24."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/RT.2007.4342598"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3307334.3328594"},{"key":"e_1_3_2_1_13_1","unstructured":"Apple Inc. 2020. Introducing ARKit 4. https:\/\/developer.apple.com\/augmented-reality\/arkit\/.  Apple Inc. 2020. Introducing ARKit 4. https:\/\/developer.apple.com\/augmented-reality\/arkit\/."},{"key":"e_1_3_2_1_14_1","volume-title":"iPad Pro","author":"Apple Inc. 2020.","year":"2020","unstructured":"Apple Inc. 2020. iPad Pro 2020 . https:\/\/www.apple.com\/ipad-pro\/specs\/. Apple Inc. 2020. iPad Pro 2020. https:\/\/www.apple.com\/ipad-pro\/specs\/."},{"key":"e_1_3_2_1_15_1","unstructured":"Inter IKEA Systems B. V. 2017. IKEA Place. https:\/\/apps.apple.com\/us\/app\/ikeaplace\/id1279244498. Accessed: 2020-7-2.  Inter IKEA Systems B. V. 2017. IKEA Place. https:\/\/apps.apple.com\/us\/app\/ikeaplace\/id1279244498. Accessed: 2020-7-2."},{"key":"e_1_3_2_1_16_1","volume-title":"Edge Assisted Real-time Object Detection for Mobile Augmented Reality. In The 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19)","author":"Liu Luyang","year":"2019","unstructured":"Luyang Liu , Hongyu Li , and Marco Gruteser . 2019 . Edge Assisted Real-time Object Detection for Mobile Augmented Reality. In The 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19) (Los Cabos, Mexico) (MobiCom '19, Article 25). Association for Computing Machinery, New York, NY, USA, 1--16. Luyang Liu, Hongyu Li, and Marco Gruteser. 2019. Edge Assisted Real-time Object Detection for Mobile Augmented Reality. In The 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19) (Los Cabos, Mexico) (MobiCom '19, Article 25). Association for Computing Machinery, New York, NY, USA, 1--16."},{"key":"e_1_3_2_1_17_1","volume-title":"DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing. In 2018 IEEE 26th International Conference on Network Protocols (ICNP). 1--11","author":"Liu Q","year":"2018","unstructured":"Q Liu and T Han . 2018 . DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing. In 2018 IEEE 26th International Conference on Network Protocols (ICNP). 1--11 . Q Liu and T Han. 2018. DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing. In 2018 IEEE 26th International Conference on Network Protocols (ICNP). 1--11."},{"key":"e_1_3_2_1_18_1","volume-title":"An Edge Network Orchestrator for Mobile Augmented Reality. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. ieeexplore.ieee.org, 756--764","author":"Liu Q","year":"2018","unstructured":"Q Liu , S Huang , J Opadere , and T Han . 2018 . An Edge Network Orchestrator for Mobile Augmented Reality. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. ieeexplore.ieee.org, 756--764 . Q Liu, S Huang, J Opadere, and T Han. 2018. An Edge Network Orchestrator for Mobile Augmented Reality. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. ieeexplore.ieee.org, 756--764."},{"key":"e_1_3_2_1_19_1","volume-title":"CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality. In 2020 19th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 301--312","author":"Liu Z","year":"2020","unstructured":"Z Liu , G Lan , J Stojkovic , Y Zhang , C Joe-Wong , and M Gorlatova . 2020 . CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality. In 2020 19th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 301--312 . Z Liu, G Lan, J Stojkovic, Y Zhang, C Joe-Wong, and M Gorlatova. 2020. CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality. In 2020 19th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 301--312."},{"key":"e_1_3_2_1_20_1","volume-title":"USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18)","author":"Samuel","unstructured":"Samuel S. Ogden and Tian Guo. 2018. MODI: Mobile Deep Inference Made Efficient by Edge Computing . In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18) . Samuel S. Ogden and Tian Guo. 2018. MODI: Mobile Deep Inference Made Efficient by Edge Computing. In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3307334.3328570"},{"key":"e_1_3_2_1_22_1","unstructured":"PyTorch. [n.d.]. Tensors and Dynamic neural networks in Python with strong GPU acceleration. https:\/\/github.com\/pytorch\/pytorch. Accessed: 2020-8-4.  PyTorch. [n.d.]. Tensors and Dynamic neural networks in Python with strong GPU acceleration. https:\/\/github.com\/pytorch\/pytorch. Accessed: 2020-8-4."},{"key":"e_1_3_2_1_23_1","volume-title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. arXiv preprint arXiv:1612.00593","author":"Qi Charles R","year":"2016","unstructured":"Charles R Qi , Hao Su , Kaichun Mo , and Leonidas J Guibas . 2016. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. arXiv preprint arXiv:1612.00593 ( 2016 ). Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2016. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. arXiv preprint arXiv:1612.00593 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Advances in Neural Information Processing Systems","author":"Qi Charles Ruizhongtai","year":"2017","unstructured":"Charles Ruizhongtai Qi , Li Yi , Hao Su , and Leonidas J Guibas . 2017. PointNet++ : Deep Hierarchical Feature Learning on Point Sets in a Metric Space . In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30 . Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/ 2017 \/file\/d8bf84be3800d12f74d8b05e9b89836f-Paper.pdf Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/d8bf84be3800d12f74d8b05e9b89836f-Paper.pdf"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383317"},{"key":"e_1_3_2_1_26_1","volume-title":"Natural environment illumination: Coherent interactive augmented reality for mobile and non-mobile devices","author":"Rohmer Kai","year":"2017","unstructured":"Kai Rohmer , Johannes Jendersie , and Thorsten Grosch . 2017. Natural environment illumination: Coherent interactive augmented reality for mobile and non-mobile devices . IEEE transactions on visualization and computer graphics 23, 11 ( 2017 ), 2474--2484. Kai Rohmer, Johannes Jendersie, and Thorsten Grosch. 2017. Natural environment illumination: Coherent interactive augmented reality for mobile and non-mobile devices. IEEE transactions on visualization and computer graphics 23, 11 (2017), 2474--2484."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/2386388.2386404"},{"key":"e_1_3_2_1_28_1","volume-title":"Neural Illumination: Lighting Prediction for Indoor Environments. CVPR","author":"Song Shuran","year":"2019","unstructured":"Shuran Song and Thomas Funkhouser . 2019 . Neural Illumination: Lighting Prediction for Indoor Environments. CVPR (2019). Shuran Song and Thomas Funkhouser. 2019. Neural Illumination: Lighting Prediction for Indoor Environments. CVPR (2019)."},{"key":"e_1_3_2_1_29_1","volume-title":"Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination. In CVPR.","author":"Srinivasan Pratul P.","year":"2020","unstructured":"Pratul P. Srinivasan , Ben Mildenhall , Matthew Tancik , Jonathan T. Barron , Richard Tucker , and Noah Snavely . 2020 . Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination. In CVPR. Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, and Noah Snavely. 2020. Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination. In CVPR."},{"key":"e_1_3_2_1_30_1","unstructured":"Unity. 2020. AR Foundation 4.2.0-preview.5. https:\/\/docs.unity3d.com\/Packages\/com.unity.xr.arfoundation@4.2\/manual\/index.html.  Unity. 2020. AR Foundation 4.2.0-preview.5. https:\/\/docs.unity3d.com\/Packages\/com.unity.xr.arfoundation@4.2\/manual\/index.html."},{"key":"e_1_3_2_1_31_1","unstructured":"Unity3D. [n.d.]. Unity Profiler. https:\/\/docs.unity3d.com\/Manual\/Profiler.html. Accessed: 2020-8-4.  Unity3D. [n.d.]. Unity Profiler. https:\/\/docs.unity3d.com\/Manual\/Profiler.html. Accessed: 2020-8-4."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00985"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Yiqin Zhao and Tian Guo. 2020. PointAR: Efficient Lighting Estimation for Mobile Augmented Reality. In Computer Vision - ECCV 2020 Andrea Vedaldi Horst Bischof Thomas Brox and Jan-Michael Frahm (Eds.). Springer International Publishing Cham 678--693.  Yiqin Zhao and Tian Guo. 2020. PointAR: Efficient Lighting Estimation for Mobile Augmented Reality. In Computer Vision - ECCV 2020 Andrea Vedaldi Horst Bischof Thomas Brox and Jan-Michael Frahm (Eds.). Springer International Publishing Cham 678--693.","DOI":"10.1007\/978-3-030-58592-1_40"}],"event":{"name":"MobiSys '21: The 19th Annual International Conference on Mobile Systems, Applications, and Services","location":"Virtual Event Wisconsin","acronym":"MobiSys '21","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"]},"container-title":["Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458864.3467886","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3458864.3467886","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3458864.3467886","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3458864.3467886","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:55Z","timestamp":1750195495000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458864.3467886"}},"subtitle":["a 3D vision-based lighting estimation framework for mobile augmented reality"],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":33,"alternative-id":["10.1145\/3458864.3467886","10.1145\/3458864"],"URL":"https:\/\/doi.org\/10.1145\/3458864.3467886","relation":{},"subject":[],"published":{"date-parts":[[2021,6,24]]},"assertion":[{"value":"2021-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}