articleJun 1, 2023Closed access

Neuralangelo: High-Fidelity Neural Surface Reconstruction

Nvidia (United Kingdom) · Johns Hopkins University

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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 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

363
total citations
FWCI
173.84
Percentile
100%
References
64
Citations per year

Authors

7

Topics & keywords

Keywords
  • Rendering (computer graphics)
  • Computer science
  • High fidelity
  • Artificial intelligence
  • Computer vision
  • Smoothing
  • Surface reconstruction
  • Iterative reconstruction
UN Sustainable Development Goals
  • Sustainable cities and communities
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