With the introduction of Neural Radiance Fields (NeRFs), novel view synthesis has recently made a big leap forward. At the core, NeRF proposes that each 3D point can emit radiance, allowing to conduct view synthesis using differentiable volumetric rendering. While neural radiance fields can accurately represent 3D scenes for computing the image rendering, 3D meshes are still the main scene representation supported by most computer graphics and simulation pipelines, enabling tasks such as real time rendering and physics-based simulations. Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field. We thus propose a novel compact and flexible architecture that enables easy 3D surface reconstruction from any NeRF-driven approach. Upon having trained the radiance field, we distill the volumetric 3D representation into a Signed Surface Approximation Network, allowing easy extraction of the 3D mesh and appearance. Our final 3D mesh is physically accurate and can be rendered in real time on an array of devices.

翻译：随着神经辐射场（NeRFs）的引入，新视角合成技术近期取得了重大突破。其核心在于，NeRF提出每个三维点都能发射辐射能量，从而通过可微分的体渲染实现视角合成。尽管神经辐射场能精确表示三维场景以完成图像渲染计算，但三维网格仍是多数计算机图形学与仿真管线所支持的主流场景表示形式，可支撑实时渲染、基于物理的仿真等任务。然而，由于NeRF针对视角合成优化，并未对辐射场施加精确的底层几何约束，因此从神经辐射场获取三维网格仍是未解决的挑战。为此，我们提出一种新型紧凑灵活架构，能够从任意NeRF驱动方法中便捷地重建三维表面。在训练辐射场后，我们将体素三维表示蒸馏至符号表面逼近网络，从而轻松提取三维网格与外观信息。最终生成的三维网格兼具物理精确性，并可在多种设备上实现实时渲染。