Neuralangelo: High-Fidelity Neural Surface Reconstruction
Nvidia (United Kingdom) · Johns Hopkins University
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 multiresolution 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 multiview…
Citation impact
- FWCI
- 173.84
- Percentile
- 100%
- References
- 64
Authors
7Topics & keywords
- Rendering (computer graphics)
- Computer science
- High fidelity
- Artificial intelligence
- Computer vision
- Smoothing
- Surface reconstruction
- Iterative reconstruction
- Sustainable cities and communities