{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:07:49Z","timestamp":1776100069942,"version":"3.50.1"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T00:00:00Z","timestamp":1532908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2018,8,31]]},"abstract":"<jats:p>Capturing aerial videos with a quadrotor-mounted camera is a challenging creative task, as it requires the simultaneous control of the quadrotor's motion and the mounted camera's orientation. Letting the drone follow a pre-planned trajectory is a much more appealing option, and recent research has proposed a number of tools designed to automate the generation of feasible camera motion plans; however, these tools typically require the user to specify and edit the camera path, for example by providing a complete and ordered sequence of key viewpoints.<\/jats:p>\n          <jats:p>\n            In this paper, we propose a higher level tool designed to enable even novice users to easily capture compelling aerial videos of large-scale outdoor scenes. Using a coarse 2.5D model of a scene, the user is only expected to specify starting and ending viewpoints and designate a set of landmarks, with or without a particular order. Our system automatically generates a diverse set of candidate local\n            <jats:italic>camera moves<\/jats:italic>\n            for observing each landmark, which are collision-free, smooth, and adapted to the shape of the landmark. These moves are guided by a landmark-centric view quality field, which combines visual interest and frame composition. An optimal global camera trajectory is then constructed that chains together a sequence of local camera moves, by choosing one move for each landmark and connecting them with suitable transition trajectories. This task is formulated and solved as an instance of the Set Traveling Salesman Problem.\n          <\/jats:p>","DOI":"10.1145\/3197517.3201284","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T15:56:23Z","timestamp":1533052583000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Creating and chaining camera moves for quadrotor videography"],"prefix":"10.1145","volume":"37","author":[{"given":"Ke","family":"Xie","sequence":"first","affiliation":[{"name":"Shenzhen University"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}]},{"given":"Shengqiu","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}]},{"given":"Dani","family":"Lischinski","sequence":"additional","affiliation":[{"name":"The Hebrew University of Jerusalem"}]},{"given":"Marc","family":"Christie","sequence":"additional","affiliation":[{"name":"Univ Rennes, INRIA"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"Shenzhen University and National University of Defense Technology"}]},{"given":"Minglun","family":"Gong","sequence":"additional","affiliation":[{"name":"Memorial University of Newfoundland"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Shenzhen University and Tel Aviv University"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}]}],"member":"320","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01928918"},{"key":"e_1_2_2_2_1","volume-title":"Proc. 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