用强化学习动态调整网格，实时重建地下复杂环境3D模型
ARMOR: Adaptive Meshing with Reinforcement Optimization for Real-time 3D Monitoring in Unexposed Scenes
- 通过强化学习自适应优化网格生成策略
- 相比顶尖方法几何误差降低3.96%，保持实时性能
- 适合地下洞穴、隧道等未知环境的实时三维监测
未探明环境如熔岩洞、矿井和隧道，是科学探索与基础设施建设中最具挑战性也最具有战略意义的领域。准确且实时的三维网格重建对自动化结构评估、机器人巡检和安全监控至关重要。隐式神经符号距离场（SDF）在在线网格重建中展现出潜力，但现有方法常存在较大投影误差，且依赖固定重建参数，难以适应隧道、洞穴和熔岩洞等复杂非结构化地下环境。为此，本文提出ARMOR——一种面向未探明环境的场景自适应、基于强化学习的实时三维网格重建框架。该方法在超过3000米的地下环境中进行了验证，涵盖人工隧道、天然洞穴和熔岩洞。实验表明，ARMOR在实时网格重建中表现优异，相比当前最优基线几何误差降低3.96%，同时保持实时效率。该方法展现出更强的鲁棒性、精度与适应性，具备在复杂未探明场景中实现先进三维监测与建图的潜力。
原文摘要 · Abstract (English)
Unexposed environments, such as lava tubes, mines, and tunnels, are among the most complex yet strategically significant domains for scientific exploration and infrastructure development. Accurate and real-time 3D meshing of these environments is essential for applications including automated structural assessment, robotic-assisted inspection, and safety monitoring. Implicit neural Signed Distance Fields (SDFs) have shown promising capabilities in online meshing; however, existing methods often suffer from large projection errors and rely on fixed reconstruction parameters, limiting their adaptability to complex and unstructured underground environments such as tunnels, caves, and lava tubes. To address these challenges, this paper proposes ARMOR, a scene-adaptive and reinforcement learning-based framework for real-time 3D meshing in unexposed environments. The proposed method was validated across more than 3,000 meters of underground environments, including engineered tunnels, natural caves, and lava tubes. Experimental results demonstrate that ARMOR achieves superior performance in real-time mesh reconstruction, reducing geometric error by 3.96\% compared to state-of-the-art baselines, while maintaining real-time efficiency. The method exhibits improved robustness, accuracy, and adaptability, indicating its potential for advanced 3D monitoring and mapping in challenging unexposed scenarios. The project page can be found at: https://yizhezhang0418.github.io/armor.github.io/
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