Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.

翻译：神经表面重建已被证明通过基于图像的神经渲染恢复密集三维表面具有强大能力。然而，现有方法在恢复真实场景的细节结构方面仍存在困难。为解决此问题，我们提出Neuralangelo，它将多分辨率三维哈希网格的表示能力与神经表面渲染相结合。两个关键要素促成了我们的方法：（1）用于计算高阶导数的数值梯度作为平滑操作；（2）在控制不同细节级别的哈希网格上进行从粗到细的优化。即使没有深度等辅助输入，Neuralangelo也能从多视图图像中有效恢复密集的三维表面结构，其保真度显著超越先前方法，从而能够从RGB视频捕获中实现详细的大规模场景重建。