Geometric Multi-Model Fitting by Deep Reinforcement Learning

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

AAAI 2019 pp. 10081-10082

doi:10.1609/AAAI.V33I01.330110081 /aaai/2019/zhang2019aaai-geometric/

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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Cite

Text

Zhang et al. "Geometric Multi-Model Fitting by Deep Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.330110081

Markdown

[Zhang et al. "Geometric Multi-Model Fitting by Deep Reinforcement Learning." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/zhang2019aaai-geometric/) doi:10.1609/AAAI.V33I01.330110081

BibTeX

@inproceedings{zhang2019aaai-geometric,
  title     = {{Geometric Multi-Model Fitting by Deep Reinforcement Learning}},
  author    = {Zhang, Zongliang and Zeng, Hongbin and Li, Jonathan and Chen, Yiping and Yang, Chenhui and Wang, Cheng},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {10081-10082},
  doi       = {10.1609/AAAI.V33I01.330110081},
  url       = {https://mlanthology.org/aaai/2019/zhang2019aaai-geometric/}
}