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arXiv · 2306.03092

Neuralangelo: High-Fidelity Neural Surface Reconstruction

Zhaoshuo Li·Thomas Müller·Alex Evans·Russell H. Taylor·Mathias Unberath·Ming-Yu Liu·Chen-Hsuan Lin
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Abstract

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.

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Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H. Taylor, Mathias Unberath, Ming-Yu Liu, Chen-Hsuan Lin. 2023-06-05. Neuralangelo: High-Fidelity Neural Surface Reconstruction. https://arxiv.org/abs/2306.03092

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