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.
@article{arxiv.2306.03092,
title = {Neuralangelo: High-Fidelity Neural Surface Reconstruction},
author = {Zhaoshuo Li and Thomas Müller and Alex Evans and Russell H. Taylor and Mathias Unberath and Ming-Yu Liu and Chen-Hsuan Lin},
journal= {arXiv preprint arXiv:2306.03092},
year = {2023}
}