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.

本文介绍了一种新的神经表面重建方法Neuralangelo，该方法结合了多分辨率3D哈希网格和神经表面渲染的表示能力。通过利用数值梯度和粗到细优化控制不同级别的细节，即使在没有深度等辅助输入的情况下，Neuralangelo也可以从多视角图像中有效地恢复密集的3D表面结构，比以前的方法实现了更高的精度，从而使得可以用RGB视频捕获对大规模场景进行详细的重建。

神经安格洛：高保真神经表面重建