Mesh reconstruction based on Neural Radiance Fields (NeRF) is popular in a variety of applications such as computer graphics, virtual reality, and medical imaging due to its efficiency in handling complex geometric structures and facilitating real-time rendering. However, existing works often fail to capture fine geometric details accurately and struggle with optimizing rendering quality. To address these challenges, we propose a novel algorithm that progressively generates and optimizes meshes from multi-view images. Our approach initiates with the training of a NeRF model to establish an initial Signed Distance Field (SDF) and a view-dependent appearance field. Subsequently, we iteratively refine the SDF through a differentiable mesh extraction method, continuously updating both the vertex positions and their connectivity based on the loss from mesh differentiable rasterization, while also optimizing the appearance representation. To further leverage high-fidelity and detail-rich representations from NeRF, we propose an online-learning strategy based on Upper Confidence Bound (UCB) to enhance viewpoints by adaptively incorporating images rendered by the initial NeRF model into the training dataset. Through extensive experiments, we demonstrate that our method delivers highly competitive and robust performance in both mesh rendering quality and geometric quality.

本研究解决了现有网格重建方法在捕捉精细几何细节和优化渲染质量上存在的不足。我们提出了一种新颖的算法，通过从多视角图像逐步生成和优化网格，并引入在线学习策略以增强视角，最终表明该方法在网格渲染和几何质量方面具备高度竞争力和稳健性。

$R^2$网格：通过几何和外观精炼的强化学习驱动的网格重建