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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_132@linklings.com
SUMMARY:Learning Reconstructability for Drone Aerial Path Planning
DESCRIPTION:Technical Papers\n\nLearning Reconstructability for Drone Aeri
 al Path Planning\n\nLiu, Lin, Hu, Xie, Fu...\n\nWe introduce the first lea
 rning-based reconstructability predictor to improve view and path planning
  for large-scale 3D urban scene acquisition using unmanned drones. In cont
 rast to previous heuristic approaches, our method learns a model that expl
 icitly predicts how well a 3D urban scene will be reconstructed from a set
  of viewpoints. To make such a model trainable and simultaneously applicab
 le to drone path planning, we simulate the proxy-based 3D scene reconstruc
 tion during training to set up the prediction. Specifically, the neural ne
 twork we design is trained to predict the scene reconstructability as a fu
 nction of the proxy geometry, 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 ge
 ometry, to guide the drone path planning. We demonstrate that our data-dri
 ven reconstructability predictions are more closely correlated to the true
  reconstruction quality than prior heuristic measures. Further, our learne
 d 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\nreal scenes.\n\nRe
 gistration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCES
 S, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_132&sess=sess15
 3
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