{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:05:06Z","timestamp":1785416706140,"version":"3.56.0"},"publisher-location":"Cham","reference-count":73,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585570","type":"print"},{"value":"9783030585587","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\/978-3-030-58558-7_24","type":"book-chapter","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T09:03:08Z","timestamp":1603875788000},"page":"400-418","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":110,"title":["Occupancy Anticipation for Efficient Exploration and Navigation"],"prefix":"10.1007","author":[{"given":"Santhosh K.","family":"Ramakrishnan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziad","family":"Al-Halah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristen","family":"Grauman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"24_CR1","unstructured":"The Habitat Challenge 2020. https:\/\/aihabitat.org\/challenge\/2020\/"},{"key":"24_CR2","unstructured":"Anderson, P., et al.: On evaluation of embodied navigation agents. arXiv preprint arXiv:1807.06757 (2018)"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Anderson, P., et al.: Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00387"},{"key":"24_CR4","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":"24_CR5","doi-asserted-by":"crossref","unstructured":"Bao, S.Y., Bagra, M., Chao, Y.W., Savarese, S.: Semantic structure from motion with points, regions, and objects. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2703\u20132710. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247992"},{"key":"24_CR6","unstructured":"Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., Efros, A.A.: Large-scale study of curiosity-driven learning. arXiv:1808.04355 (2018)"},{"issue":"6","key":"24_CR7","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","volume":"32","author":"C Cadena","year":"2016","unstructured":"Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309\u20131332 (2016)","journal-title":"IEEE Trans. Rob."},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Carrillo, H., Reid, I., Castellanos, J.A.: On the comparison of uncertainty criteria for active slam. In: 2012 IEEE International Conference on Robotics and Automation, pp. 2080\u20132087. IEEE (2012)","DOI":"10.1109\/ICRA.2012.6224890"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Chang, A., et al.: Matterport3D: learning from RGB-D data in indoor environments. In: Proceedings of the International Conference on 3D Vision (3DV), MatterPort3D dataset license (2017). http:\/\/kaldir.vc.in.tum.de\/matterport\/MP_TOS.pdf","DOI":"10.1109\/3DV.2017.00081"},{"key":"24_CR10","unstructured":"Chaplot, D.S., Gupta, S., Gandhi, D., Gupta, A., Salakhutdinov, R.: Learning to explore using active neural mapping. In: 8th International Conference on Learning Representations, ICLR 2020 (2020)"},{"key":"24_CR11","unstructured":"Chen, T., Gupta, S., Gupta, A.: Learning exploration policies for navigation. In: 7th International Conference on Learning Representations, ICLR 2019 (2019)"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., Batra, D.: Embodied question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2054\u20132063 (2018)","DOI":"10.1109\/CVPR.2018.00008"},{"key":"24_CR13","unstructured":"Datta, S., Maksymets, O., Hoffman, J., Lee, S., Batra, D., Parikh, D.: Integrating egocentric localization for more realistic pointgoal navigation agents. In: CVPR 2020 Embodied AI Workshop (2020)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Dhamo, H., Navab, N., Tombari, F.: Object-driven multi-layer scene decomposition from a single image. In: The IEEE International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00547"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Elhafsi, A., Ivanovic, B., Janson, L., Pavone, M.: Map-predictive motion planning in unknown environments. arXiv preprint arXiv:1910.08184 (2019)","DOI":"10.1109\/ICRA40945.2020.9197522"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Fang, K., Toshev, A., Fei-Fei, L., Savarese, S.: Scene memory transformer for embodied agents in long-horizon tasks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 538\u2013547 (2019)","DOI":"10.1109\/CVPR.2019.00063"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Gan, C., Zhang, Y., Wu, J., Gong, B., Tenenbaum, J.B.: Look, listen, and act: towards audio-visual embodied navigation. arXiv preprint arXiv:1912.11684 (2019)","DOI":"10.1109\/ICRA40945.2020.9197008"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., Farhadi, A.: IQA: visual question answering in interactive environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4089\u20134098 (2018)","DOI":"10.1109\/CVPR.2018.00430"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Gupta, S., Davidson, J., Levine, S., Sukthankar, R., Malik, J.: Cognitive mapping and planning for visual navigation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2616\u20132625 (2017)","DOI":"10.1109\/CVPR.2017.769"},{"key":"24_CR20","unstructured":"Gupta, S., Fouhey, D., Levine, S., Malik, J.: Unifying map and landmark based representations for visual navigation. arXiv preprint arXiv:1712.08125 (2017)"},{"key":"24_CR21","volume-title":"Multiple View Geometry in Computer Vision","author":"R Hartley","year":"2003","unstructured":"Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Vedaldi, A.: MapNet: an allocentric spatial memory for mapping environments. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8476\u20138484 (2018)","DOI":"10.1109\/CVPR.2018.00884"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Hoermann, S., Bach, M., Dietmayer, K.: Dynamic occupancy grid prediction for urban autonomous driving: a deep learning approach with fully automatic labeling. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2056\u20132063. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460874"},{"issue":"4","key":"24_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073659","volume":"36","author":"S Iizuka","year":"2017","unstructured":"Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. (ToG) 36(4), 1\u201314 (2017)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Jayaraman, D., Gao, R., Grauman, K.: ShapeCodes: self-supervised feature learning by lifting views to viewgrids. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 120\u2013136 (2018)","DOI":"10.1007\/978-3-030-01270-0_8"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Jayaraman, D., Grauman, K.: Learning to look around: intelligently exploring unseen environments for unknown tasks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00135"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Chen, J., Liu, C., Wu, J., Furukawa, Y.: Floor-SP: inverse CAD for floorplans by sequential room-wise shortest path. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00275"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Karkus, P., Ma, X., Hsu, D., Kaelbling, L.P., Lee, W.S., Lozano-P\u00e9rez, T.: Differentiable algorithm networks for composable robot learning. arXiv preprint arXiv:1905.11602 (2019)","DOI":"10.15607\/RSS.2019.XV.039"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Katyal, K., Popek, K., Paxton, C., Burlina, P., Hager, G.D.: Uncertainty-aware occupancy map prediction using generative networks for robot navigation. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 5453\u20135459. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793500"},{"key":"24_CR30","unstructured":"Katyal, K., et al.: Occupancy map prediction using generative and fully convolutional networks for vehicle navigation. arXiv preprint arXiv:1803.02007 (2018)"},{"key":"24_CR31","unstructured":"Kolve, E., et al.: AI2-THOR: An Interactive 3D Environment for Visual AI. arXiv (2017)"},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, S., Yang, J., Yang, M.H.: Generative face completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3911\u20133919 (2017)","DOI":"10.1109\/CVPR.2017.624"},{"key":"24_CR33","doi-asserted-by":"crossref","unstructured":"Liu, C., Wu, J., Furukawa, Y.: FloorNet: a unified framework for floorplan reconstruction from 3D scans. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 201\u2013217 (2018)","DOI":"10.1007\/978-3-030-01231-1_13"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Lu, C., Dubbelman, G.: Hallucinating beyond observation: learning to complete with partial observation and unpaired prior knowledge (2019)","DOI":"10.1016\/j.patcog.2020.107426"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Savva, M., et al.: Habitat: a platform for embodied AI research. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00943"},{"issue":"2","key":"24_CR36","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s10514-009-9130-2","volume":"27","author":"R Martinez-Cantin","year":"2009","unstructured":"Martinez-Cantin, R., De Freitas, N., Brochu, E., Castellanos, J., Doucet, A.: A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Auton. Rob. 27(2), 93\u2013103 (2009)","journal-title":"Auton. Rob."},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Mohajerin, N., Rohani, M.: Multi-step prediction of occupancy grid maps with recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10600\u201310608 (2019)","DOI":"10.1109\/CVPR.2019.01085"},{"key":"24_CR38","doi-asserted-by":"crossref","unstructured":"Mousavian, A., Toshev, A., Fi\u0161er, M., Ko\u0161eck\u00e1, J., Wahid, A., Davidson, J.: Visual representations for semantic target driven navigation. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 8846\u20138852. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793493"},{"key":"24_CR39","unstructured":"M\u00fcller, M., Dosovitskiy, A., Ghanem, B., Koltun, V.: Driving policy transfer via modularity and abstraction. arXiv preprint arXiv:1804.09364 (2018)"},{"issue":"1","key":"24_CR40","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1177\/0278364911421039","volume":"31","author":"ST O\u2019Callaghan","year":"2012","unstructured":"O\u2019Callaghan, S.T., Ramos, F.T.: Gaussian process occupancy maps. Int. J. Robot. Res. 31(1), 42\u201362 (2012)","journal-title":"Int. J. Robot. Res."},{"key":"24_CR41","unstructured":"Parisotto, E., Salakhutdinov, R.: Neural map: structured memory for deep reinforcement learning. arXiv preprint arXiv:1702.08360 (2017)"},{"key":"24_CR42","doi-asserted-by":"crossref","unstructured":"Pathak, D., Agrawal, P., Efros, A.A., Darrell, T.: Curiosity-driven exploration by self-supervised prediction. In: International Conference on Machine Learning (2017)","DOI":"10.1109\/CVPRW.2017.70"},{"key":"24_CR43","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.278"},{"key":"24_CR44","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, S.K., Grauman, K.: Sidekick policy learning for active visual exploration. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 413\u2013430 (2018)","DOI":"10.1007\/978-3-030-01258-8_26"},{"key":"24_CR45","doi-asserted-by":"publisher","unstructured":"Ramakrishnan, S.K., Jayaraman, D., Grauman, K.: Emergence of exploratory look-around behaviors through active observation completion. Sci. Robot. 4(30) (2019). https:\/\/doi.org\/10.1126\/scirobotics.aaw6326, https:\/\/robotics.sciencemag.org\/content\/4\/30\/eaaw6326","DOI":"10.1126\/scirobotics.aaw6326"},{"key":"24_CR46","unstructured":"Ramakrishnan, S.K., Jayaraman, D., Grauman, K.: An exploration of embodied visual exploration. arXiv preprint arXiv:2001.02192 (2020)"},{"issue":"14","key":"24_CR47","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1177\/0278364916684382","volume":"35","author":"F Ramos","year":"2016","unstructured":"Ramos, F., Ott, L.: Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent. Int. J. Robot. Res. 35(14), 1717\u20131730 (2016)","journal-title":"Int. J. Robot. Res."},{"key":"24_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"24_CR49","doi-asserted-by":"crossref","unstructured":"Salas-Moreno, R.F., Newcombe, R.A., Strasdat, H., Kelly, P.H., Davison, A.J.: Slam++: simultaneous localisation and mapping at the level of objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1352\u20131359 (2013)","DOI":"10.1109\/CVPR.2013.178"},{"key":"24_CR50","unstructured":"Savinov, N., Dosovitskiy, A., Koltun, V.: Semi-parametric topological memory for navigation. arXiv preprint arXiv:1803.00653 (2018)"},{"key":"24_CR51","unstructured":"Savinov, N., et al.: Episodic curiosity through reachability. arXiv preprint arXiv:1810.02274 (2018)"},{"key":"24_CR52","unstructured":"Savva, M., Chang, A.X., Dosovitskiy, A., Funkhouser, T., Koltun, V.: MINOS: multimodal indoor simulator for navigation in complex environments. arXiv preprint arXiv:1712.03931 (2017)"},{"key":"24_CR53","unstructured":"Sax, A., Emi, B., Zamir, A.R., Guibas, L., Savarese, S., Malik, J.: Mid-level visual representations improve generalization and sample efficiency for learning visuomotor policies. arXiv preprint arXiv:1812.11971 (2018)"},{"key":"24_CR54","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"24_CR55","unstructured":"Seifi, S., Tuytelaars, T.: Where to look next: unsupervised active visual exploration on 360 $$\\{\\backslash $$deg$$\\}$$ input. arXiv preprint arXiv:1909.10304 (2019)"},{"key":"24_CR56","unstructured":"Senanayake, R., Ganegedara, T., Ramos, F.: Deep occupancy maps: a continuous mapping technique for dynamic environments (2017)"},{"key":"24_CR57","doi-asserted-by":"crossref","unstructured":"Shen, W.B., Xu, D., Zhu, Y., Guibas, L.J., Fei-Fei, L., Savarese, S.: Situational fusion of visual representation for visual navigation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2881\u20132890 (2019)","DOI":"10.1109\/ICCV.2019.00297"},{"key":"24_CR58","doi-asserted-by":"crossref","unstructured":"Shrestha, R., Tian, F.P., Feng, W., Tan, P., Vaughan, R.: Learned map prediction for enhanced mobile robot exploration. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 1197\u20131204. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793769"},{"key":"24_CR59","unstructured":"Sless, L., Cohen, G., Shlomo, B.E., Oron, S.: Self supervised occupancy grid learning from sparse radar for autonomous driving. arXiv preprint arXiv:1904.00415 (2019)"},{"key":"24_CR60","doi-asserted-by":"crossref","unstructured":"Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.28"},{"key":"24_CR61","doi-asserted-by":"crossref","unstructured":"Song, S., Zeng, A., Chang, A.X., Savva, M., Savarese, S., Funkhouser, T.: Im2pano3D: extrapolating 360 structure and semantics beyond the field of view. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3847\u20133856 (2018)","DOI":"10.1109\/CVPR.2018.00405"},{"key":"24_CR62","unstructured":"Straub, J., et al.: The replica dataset: a digital replica of indoor spaces. arXiv preprint arXiv:1906.05797 (2019)"},{"key":"24_CR63","doi-asserted-by":"crossref","unstructured":"Sun, C., Hsiao, C.W., Sun, M., Chen, H.T.: HorizonNet: learning room layout with 1D representation and pano stretch data augmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00114"},{"issue":"3","key":"24_CR64","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1145\/504729.504754","volume":"45","author":"S Thrun","year":"2002","unstructured":"Thrun, S.: Probabilistic robotics. Commun. ACM 45(3), 52\u201357 (2002)","journal-title":"Commun. ACM"},{"key":"24_CR65","unstructured":"Wijmans, E., Kadian, A., Morcos, A., Lee, S., Essa, I., Parikh, D., Savva, M., Batra, D.: DD-PPO: learning near-perfect pointgoal navigators from 2.5 billion frames (2020)"},{"issue":"6","key":"24_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356556","volume":"38","author":"W Wu","year":"2019","unstructured":"Wu, W., Fu, X.M., Tang, R., Wang, Y., Qi, Y.H., Liu, L.: Data-driven interior plan generation for residential buildings. ACM Trans. Graph. 38(6), 1\u20132 (2019). https:\/\/doi.org\/10.1145\/3355089.3356556","journal-title":"ACM Trans. Graph."},{"key":"24_CR67","unstructured":"Xia, F., et al.: Gibson Env: real-world perception for embodied agents. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9068\u20139079, Gibson dataset license agreement (2018). https:\/\/storage.googleapis.com\/gibson_material\/Agreement%20GDS%2006-04-18.pdf"},{"key":"24_CR68","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Embodied amodal recognition: learning to move to perceive objects. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00213"},{"key":"24_CR69","doi-asserted-by":"crossref","unstructured":"Yang, S.T., Wang, F.E., Peng, C.H., Wonka, P., Sun, M., Chu, H.K.: DuLa-Net: a dual-projection network for estimating room layouts from a single RGB panorama. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3363\u20133372 (2019)","DOI":"10.1109\/CVPR.2019.00348"},{"key":"24_CR70","unstructured":"Yang, W., Wang, X., Farhadi, A., Gupta, A., Mottaghi, R.: Visual semantic navigation using scene priors. arXiv preprint arXiv:1810.06543 (2018)"},{"key":"24_CR71","doi-asserted-by":"crossref","unstructured":"Yang, Z., Pan, J.Z., Luo, L., Zhou, X., Grauman, K., Huang, Q.: Extreme relative pose estimation for RGB-D scans via scene completion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00466"},{"key":"24_CR72","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Visual semantic planning using deep successor representations. In: 2017 IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.60"},{"key":"24_CR73","doi-asserted-by":"crossref","unstructured":"Zou, C., Colburn, A., Shan, Q., Hoiem, D.: LayoutNet: reconstructing the 3D room layout from a single RGB image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2051\u20132059 (2018)","DOI":"10.1109\/CVPR.2018.00219"}],"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\/978-3-030-58558-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T08:53:50Z","timestamp":1730105630000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58558-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585570","9783030585587"],"references-count":73,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58558-7_24","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":"29 October 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)"}}]}}