Geometric Multi-Model Fitting by Deep Reinforcement Learning
2019 ◽
Vol 33
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pp. 10081-10082
Keyword(s):
This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.
2019 ◽
Vol 33
◽
pp. 8393-8400
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2020 ◽
Vol 34
(07)
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pp. 12717-12724
Keyword(s):
2021 ◽
Vol 20
(02)
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pp. 2150011
2018 ◽
Vol 21
◽
pp. 29-36
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Keyword(s):