  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.
