Neuralangelo: High-Fidelity Neural Surface Reconstruction
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.
Cite
Text
Li et al. "Neuralangelo: High-Fidelity Neural Surface Reconstruction." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00817Markdown
[Li et al. "Neuralangelo: High-Fidelity Neural Surface Reconstruction." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/li2023cvpr-neuralangelo/) doi:10.1109/CVPR52729.2023.00817BibTeX
@inproceedings{li2023cvpr-neuralangelo,
title = {{Neuralangelo: High-Fidelity Neural Surface Reconstruction}},
author = {Li, Zhaoshuo and Müller, Thomas and Evans, Alex and Taylor, Russell H. and Unberath, Mathias and Liu, Ming-Yu and Lin, Chen-Hsuan},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2023},
pages = {8456-8465},
doi = {10.1109/CVPR52729.2023.00817},
url = {https://mlanthology.org/cvpr/2023/li2023cvpr-neuralangelo/}
}