Geometric Multi-Model Fitting by Deep Reinforcement Learning.

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

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.