Lightweight Online Reinforcement Learning for Block Decomposition of CAD Models

Decomposing CAD models into sweepable sub-blocks is acritical preprocessing step for high-quality hexahedral mesh generation.Existing block decomposition methods fall into two main categories: tra-ditional geometry-driven approaches and reinforcement learning (RL)-based methods. Traditional methods rely heavily on manual expertiseand interactive operations, resulting in intensive manual labor and ex-tremely low automation efficiency. Existing RL-based methods are ei-ther only applicable to simple 2D models, or dependent on large-scaleoffline datasets and pre-training, leading to high training costs and diffi-cult industrial deployment. To address these challenges, we propose thefirst pure online RL framework for 3D CAD model block decomposition.Specifically, we formulate the task as a tree-structured Markov Deci-sion Process (MDP), and adopt two core designs: a decoupled frozengeometric-topological feature extraction architecture for lightweight RLnetwork design, and a continuous-to-discrete action projection mecha-nism to ensure geometric validity of decomposition operations. Withoutrelying on any pre-training or offline datasets, our agent learns optimalcutting strategies purely through online interaction with a single targetCAD model. Experiments on our self-built dataset and public datasetsshow that our method outperforms the state-of-the-art offline RL base-line, and achieves competitive decomposition quality comparable to thehybrid fine-tuning paradigm with no pre-training dependency.