We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend to generate noisy meshes with poor topology. Differentiable rasterization-based methods can generate high-quality meshes but are sensitive to initialization. Our method combines the benefits of both worlds; we take the geometry initialization obtained from neural volumetric fields, and further optimize the geometry as well as a compact neural texture representation with differentiable rasterizers. Through extensive experiments, we demonstrate that our method can generate accurate mesh reconstructions with faithful appearance that are comparable to previous volume rendering methods while being an order of magnitude faster in rendering. We also show that our generated mesh and neural texture reconstruction is compatible with existing graphics pipelines and enables downstream 3D applications such as simulation. Project page: https://sarahweiii.github.io/neumanifold/

翻译：我们提出了一种从多视角输入图像生成高质量水密流形网格的方法。现有体积渲染方法在优化过程中具有鲁棒性，但倾向于生成拓扑结构较差的噪声网格。基于可微分光栅化的方法虽能生成高质量网格，但对初始化敏感。我们的方法融合了二者的优势：首先从神经体积场获取几何初始化，再利用可微分光栅化器进一步优化几何以及紧凑的神经纹理表示。通过大量实验证明，该方法能够生成与先前体积渲染方法精度相当的真实外观重建，同时渲染速度提升一个数量级。此外，我们生成的网格与神经纹理重建兼容现有图形管线，可支持仿真等下游三维应用。项目页面：https://sarahweiii.github.io/neumanifold/