Geometric Multi-Model Fitting by Deep Reinforcement Learning
Computer Vision and Pattern Recognition
2018-12-31 v2 Machine Learning
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.
Cite
@article{arxiv.1809.08397,
title = {Geometric Multi-Model Fitting by Deep Reinforcement Learning},
author = {Zongliang Zhang and Hongbin Zeng and Jonathan Li and Yiping Chen and Chenhui Yang and Cheng Wang},
journal= {arXiv preprint arXiv:1809.08397},
year = {2018}
}