NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support

While existing volumetric rendering approaches provide photorealistic results, extracting high-quality meshes from optimized neural field representations is challenging. Conversely, existing differentiable rasterization-based methods are typically sensitive to initialization and suffer from poor mesh rendering quality. In this paper, we introduce Neu-Manifold, a novel method for reconstructing watertight manifold meshes with high-quality textures from multi-view input images. NeuManifold overcomes the limitations of existing approaches by first learning a neural volumetric field and then refining it through differentiable mesh extraction and surface rendering. To eliminate artifacts and preserve mesh properties during iso-surface extraction, we introduce a novel differentiable marching cubes method. Instead of traditional textures, we use neural textures to enhance rendering quality. To integrate with modern graphics rendering pipelines, we also provide customized GLSL shader support for neural textures. Extensive experiments demonstrate that NeuManifold outperforms existing mesh-based reconstruction methods in both mesh quality and rendering metrics, achieving comparable or superior rendering quality to prior volume-rendering-based methods. The generated results enable real-time, high-quality rendering and seamlessly support numerous graphics pipelines and applications requiring high-quality meshes, such as 3D printing and physical simulation. https://sarahweiii.github.io/neumanifold/.