  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.
