Geometric Multi-Model Fitting by Deep Reinforcement Learning
This work addresses the challenge of efficiently fitting multiple geometric models to unstructured point data, such as from laser scans, which is incremental as it applies a novel reinforcement learning approach to an existing problem.
The paper tackled the problem of geometric multi-model fitting from noisy point clouds by formulating it as a sequential decision process and using deep reinforcement learning to optimize decisions, resulting in a significant reduction in fitting iterations compared to state-of-the-art methods on simulated data.
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.