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Graph."],"published-print":{"date-parts":[[2023,12,5]]},"abstract":"<jats:p>\n            We introduce an\n            <jats:italic toggle=\"yes\">active 3D reconstruction<\/jats:italic>\n            method which integrates visual perception,\n            <jats:italic toggle=\"yes\">robot-object interaction<\/jats:italic>\n            , and 3D scanning to recover both the exterior and\n            <jats:italic toggle=\"yes\">interior<\/jats:italic>\n            , i.e., unexposed, geometries of a target 3D object. Unlike other works in active vision which focus on optimizing camera viewpoints to better investigate the environment, the primary feature of our reconstruction is an analysis of the\n            <jats:italic toggle=\"yes\">interactability<\/jats:italic>\n            of various parts of the target object and the ensuing part manipulation by a robot to enable scanning of occluded regions. As a result, an understanding of part articulations of the target object is obtained on top of complete geometry acquisition. Our method operates\n            <jats:italic toggle=\"yes\">fully automatically<\/jats:italic>\n            by a Fetch robot with built-in RGBD sensors. It iterates between interaction analysis and interaction-driven reconstruction, scanning and reconstructing detected moveable parts one at a time, where both the articulated part detection and mesh reconstruction are carried out by neural networks. In the final step, all the remaining, non-articulated parts, including all the interior structures that had been exposed by prior part manipulations and subsequently scanned, are reconstructed to complete the acquisition. We demonstrate the performance of our method via qualitative and quantitative evaluation, ablation studies, comparisons to alternatives, as well as experiments in a real environment.\n          <\/jats:p>","DOI":"10.1145\/3618327","type":"journal-article","created":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T10:20:48Z","timestamp":1701771648000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Interaction-Driven Active 3D Reconstruction with Object Interiors"],"prefix":"10.1145","volume":"42","author":[{"given":"Zihao","family":"Yan","sequence":"first","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fubao","family":"Su","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhen","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,5]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00133571"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12802"},{"key":"e_1_2_2_3_1","volume-title":"ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012 [cs]","author":"Chang Angel X.","year":"2015","unstructured":"Angel X. 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