GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

TL;DR

This paper introduces a novel prior-free reinforcement learning approach using graph neural networks for mesh smoothing, achieving state-of-the-art results without relying on labeled data or prior knowledge.

Contribution

It proposes a new mesh smoothing model that integrates GNNs with reinforcement learning and introduces a mesh connectivity improvement agent, trained without prior data.

Findings

Achieves feature-preserving smoothing on complex 3D meshes.

Outperforms existing intelligent smoothing methods on 2D meshes.

Runs 7.16 times faster than traditional methods.

Abstract

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models' learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh.