Mesh reconstruction from Neural Radiance Fields (NeRF) is widely used in 3D reconstruction and has been applied across numerous domains. However, existing methods typically rely solely on the given training set images, which restricts supervision to limited observations and makes it difficult to fully constrain geometry and appearance. Moreover, the contribution of each viewpoint for training is not uniform and changes dynamically during the optimization process, which can result in suboptimal guidance for both geometric refinement and rendering quality. To address these limitations, we propose $R^2$-Mesh, a reinforcement learning framework that combines NeRF-rendered pseudo-supervision with online viewpoint selection. Our key insight is to exploit NeRF's rendering ability to synthesize additional high-quality images, enriching training with diverse viewpoint information. To ensure that supervision focuses on the most beneficial perspectives, we introduce a UCB-based strategy with a geometry-aware reward, which dynamically balances exploration and exploitation to identify informative viewpoints throughout training. Within this framework, we jointly optimize SDF geometry and view-dependent appearance under differentiable rendering, while periodically refining meshes to capture fine geometric details. Experiments demonstrate that our method achieves competitive results in both geometric accuracy and rendering quality.

翻译：从神经辐射场（NeRF）重建网格被广泛应用于三维重建，并已在众多领域得到应用。然而，现有方法通常仅依赖于给定的训练集图像，这限制了监督信息只能来源于有限的观测视角，从而难以充分约束几何与外观。此外，每个视角对训练的贡献并非均匀，且在优化过程中动态变化，这可能导致对几何细化和渲染质量的指导效果欠佳。为应对这些局限，我们提出了$R^2$-Mesh，一个结合NeRF渲染伪监督与在线视角选择的强化学习框架。我们的核心思路是利用NeRF的渲染能力合成额外的高质量图像，从而通过多样化的视角信息丰富训练数据。为确保监督聚焦于最有益的视角，我们引入了一种基于UCB的策略，并配合几何感知奖励，该策略在训练过程中动态平衡探索与利用，以识别信息丰富的视角。在此框架内，我们在可微分渲染下联合优化SDF几何与视角相关的外观，同时周期性地细化网格以捕捉精细的几何细节。实验表明，我们的方法在几何精度和渲染质量方面均取得了具有竞争力的结果。