{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T17:00:38Z","timestamp":1793206838692,"version":"4.5.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["RCJC20200714114435012"],"award-info":[{"award-number":["RCJC20200714114435012"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["U2001206"],"award-info":[{"award-number":["U2001206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"GD Science and Technology Program","award":["2020A0505100064, 2015A030312015"],"award-info":[{"award-number":["2020A0505100064, 2015A030312015"]}]},{"name":"GD Talent Program","award":["2019JC05X328"],"award-info":[{"award-number":["2019JC05X328"]}]},{"name":"DEGP Key Project","award":["2018KZDXM058"],"award-info":[{"award-number":["2018KZDXM058"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>\n            We introduce the first\n            <jats:italic>path-oriented<\/jats:italic>\n            drone trajectory planning algorithm, which performs\n            <jats:italic>continuous<\/jats:italic>\n            (i.e.,\n            <jats:italic>dense<\/jats:italic>\n            ) image acquisition along an aerial path and explicitly factors\n            <jats:italic>path quality<\/jats:italic>\n            into an optimization along with scene reconstruction quality. Specifically, our method takes as input a rough 3D scene proxy and produces a drone trajectory and image capturing setup, which efficiently yields a high-quality reconstruction of the 3D scene based on three optimization objectives: one to maximize the\n            <jats:italic>amount of 3D scene information<\/jats:italic>\n            that can be acquired along the entirety of the trajectory, another to optimize the scene capturing\n            <jats:italic>efficiency<\/jats:italic>\n            by maximizing the scene information that can be acquired per unit length along the aerial path, and the last one to minimize the total\n            <jats:italic>turning angles<\/jats:italic>\n            along the aerial path, so as to reduce the number of sharp turns. Our search scheme is based on the rapidly-exploring random tree framework, resulting in a final trajectory as a single path through the search tree. Unlike state-of-the-art works, our joint optimization for view selection and path planning is performed in a\n            <jats:italic>single step.<\/jats:italic>\n            We comprehensively evaluate our method not only on benchmark virtual datasets as in existing works but also on several large-scale real urban scenes. We demonstrate that the continuous paths optimized by our method can effectively reduce onsite acquisition cost using drones, while achieving high-fidelity 3D reconstruction, compared to existing planning methods and oblique photography, a mature and popular industry solution.\n          <\/jats:p>","DOI":"10.1145\/3478513.3480483","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:28:45Z","timestamp":1639160925000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Continuous aerial path planning for 3D urban scene reconstruction"],"prefix":"10.1145","volume":"40","author":[{"given":"Han","family":"Zhang","sequence":"first","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucong","family":"Yao","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Wing","family":"Fu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, 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":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICUAS.2017.7991461"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/11861898_66"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487281"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539802"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1561\/0600000052"},{"key":"e_1_2_2_6_1","volume-title":"Proc. of IEEE Int. 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