神经安琪洛：高保真神经表面重建

摘要

神经表面重建已被证明在通过基于图像的神经渲染恢复密集3D表面方面非常强大。然而，当前方法在恢复真实场景的详细结构方面存在困难。为了解决这一问题，我们提出了Neuralangelo，它将多分辨率3D哈希网格的表示能力与神经表面渲染相结合。两个关键要素使我们的方法成为可能：(1) 用于计算高阶导数的数值梯度作为平滑操作，以及 (2) 在控制不同细节级别的哈希网格上进行由粗到细的优化。即使没有深度等辅助输入，Neuralangelo也能够从多视图图像中有效恢复密集的3D表面结构，其保真度显著超过先前的方法，从而实现了从RGB视频捕获中对大规模场景进行详细重建。

English

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.

神经安琪洛：高保真神经表面重建

Neuralangelo: High-Fidelity Neural Surface Reconstruction

摘要

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