Summary of Gnnrl-smoothing: a Prior-free Reinforcement Learning Model For Mesh Smoothing, by Zhichao Wang et al.
GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing
by Zhichao Wang, Xinhai Chen, Chunye Gong, Bo Yang, Liang Deng, Yufei Sun, Yufei Pang, Jie Liu
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a prior-free reinforcement learning model for intelligent mesh smoothing, which integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces a mesh connectivity improvement agent. The proposed model formalizes mesh optimization as a Markov Decision Process and trains both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network without prior data or knowledge. Experimental results demonstrate that the model achieves feature-preserving smoothing on complex 3D surface meshes, state-of-the-art results among intelligent smoothing methods on 2D meshes, and is 7.16 times faster than traditional optimization-based smoothing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer simulations better by creating a new way to smooth out mesh problems. Usually, people use labeled data or prior knowledge to train models for this task, but this method doesn’t need any of that. It uses a special kind of AI called reinforcement learning and combines it with a type of neural network called graph neural networks. The model is tested on 2D and 3D meshes and works really well, even on complex shapes. |
Keywords
» Artificial intelligence » Neural network » Optimization » Reinforcement learning
