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arXiv · 1809.08397

Geometric Multi-Model Fitting by Deep Reinforcement Learning

Zongliang Zhang·Hongbin Zeng·Jonathan Li·Yiping Chen·Chenhui Yang·Cheng Wang
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Abstract

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.

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Zongliang Zhang, Hongbin Zeng, Jonathan Li, Yiping Chen, Chenhui Yang, Cheng Wang. 2018-09-22. Geometric Multi-Model Fitting by Deep Reinforcement Learning. https://arxiv.org/abs/1809.08397

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