神經安琪洛：高保真度神經表面重建

摘要

神經表面重建已被證明在通過基於圖像的神經渲染恢復密集3D表面方面非常強大。然而，目前的方法在恢復真實場景的細節結構方面存在困難。為解決此問題，我們提出了Neuralangelo，它結合了多分辨率3D哈希網格的表示能力與神經表面渲染。我們方法的兩個關鍵要素包括：(1)用於計算高階導數的數值梯度作為平滑操作，以及(2)在控制不同細節級別的哈希網格上進行由粗到細的優化。即使沒有輔助輸入，如深度，Neuralangelo也能從多視圖圖像中有效地恢復密集3D表面結構，其保真度顯著超越先前的方法，實現從RGB視頻捕獲中對大規模場景進行詳細重建。

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.

神經安琪洛：高保真度神經表面重建

Neuralangelo: High-Fidelity Neural Surface Reconstruction

摘要

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