{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:51:29Z","timestamp":1763664689561,"version":"3.37.3"},"reference-count":17,"publisher":"American Institute of Aeronautics and Astronautics (AIAA)","issue":"4","funder":[{"DOI":"10.13039\/100005753","name":"New York Space Grant Consortium","doi-asserted-by":"publisher","award":["NNX15AK07H"],"award-info":[{"award-number":["NNX15AK07H"]}],"id":[{"id":"10.13039\/100005753","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["arc.aiaa.org"],"crossmark-restriction":true},"short-container-title":["Journal of Aerospace Information Systems"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:p> Neural networks have become state-of-the-art computer vision tools for tasks that learn implicit representations of geometrical scenes. This paper proposes a two-part network architecture that exploits a view-synthesis network to understand a context scene and a graph convolutional network to generate a shape body model of an object within the field of view of a spacecraft\u2019s optical navigation sensors. Once the first part of the network\u2019s architecture understands the spacecraft\u2019s environment, it can generate images from novel observations. The second part uses a multiview set of images to construct a 3D graph-based representation of the object. The proposed network pipeline produces shape models with accuracies that compete with state-of-the-art methods currently used for missions to small bodies. The network pipeline can be trained for multi-environment missions. Moreover, the onboard implementation may be more cost-effective than the current state-of-the-art. <\/jats:p>","DOI":"10.2514\/1.i011014","type":"journal-article","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T11:13:42Z","timestamp":1638011622000},"page":"259-270","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Online Shape Modeling of Resident Space Objects Through Implicit Scene Understanding"],"prefix":"10.2514","volume":"19","author":[{"given":"Aneesh M.","family":"Heintz","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, New York 14850"}]},{"given":"Mason","family":"Peck","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York 14850"}]},{"given":"Fangchen","family":"Sun","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York 14850"}]},{"given":"Ian","family":"Mackey","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York 14850"}]}],"member":"1387","reference":[{"volume-title":"Stereographic Projection Techniques for Geologists and Civil Engineers","author":"Lisle R. 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X.FunkhouserT.GuibasL.HanrahanP.HuangQ.LiZ.SavareseS.SavvaM.SongS.SuH.XiaoJ.YiL.YuF. \u201cShapeNet: An Information-Rich 3D Model Repository,\u201d Stanford University\u2013Princeton University\u2013Toyota Technological Institute at Chicago TR arXiv:1512.03012 [cs.GR], 2015."},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.2514\/6.2020-1909"},{"key":"r23","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar6170"},{"key":"r34","doi-asserted-by":"publisher","DOI":"10.1016\/j.icarus.2013.05.028"},{"key":"r36","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-1035(02)00042-8"},{"key":"r37","doi-asserted-by":"publisher","DOI":"10.1016\/j.icarus.2010.01.035"},{"volume-title":"Blender\u2014A 3D Modelling and Rendering Package","year":"2018","author":"Community B. 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