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Graph."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p>\n            We introduce the first\n            <jats:italic>learning-based reconstructability predictor<\/jats:italic>\n            to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast to previous heuristic approaches, our method learns a model that\n            <jats:italic>explicitly<\/jats:italic>\n            predicts how well a 3D urban scene will be reconstructed from a set of viewpoints. To make such a model trainable and simultaneously applicable to drone path planning, we simulate the proxy-based 3D scene reconstruction during training to set up the prediction. Specifically, the neural network we design is trained to predict the scene reconstructability as a function of the\n            <jats:italic>proxy geometry<\/jats:italic>\n            , a set of viewpoints, and optionally a series of scene images acquired in flight. To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning. We demonstrate that our data-driven reconstructability predictions are more closely correlated to the true reconstruction quality than prior heuristic measures. Further, our learned predictor can be easily integrated into existing path planners to yield improvements. Finally, we devise a new iterative view planning framework, based on the learned reconstructability, and show superior performance of the new planner when reconstructing both synthetic and real scenes.\n          <\/jats:p>","DOI":"10.1145\/3550454.3555433","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T21:19:07Z","timestamp":1669843147000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Learning Reconstructability for Drone Aerial Path Planning"],"prefix":"10.1145","volume":"41","author":[{"given":"Yilin","family":"Liu","sequence":"first","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Hu","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":[[2022,11,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_2_2_2_1","volume-title":"ShapeNet: An Information-Rich 3D Model Repository. arXiv preprint arXiv:1512.03012","author":"Chang Angel X.","year":"2015","unstructured":"Angel X. 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