Neuralangelo: High-Fidelity Neural Surface Reconstruction

PubDate: May 2023

Teams: NVIDIA Research 2Johns Hopkins University

Writers: Zhaoshuo Li1,2;Thomas Müller1;Alex Evans1;Russell H. Taylor2;Mathias Unberath2;Ming-Yu Liu1;Chen-Hsuan Lin1

PDF: Neuralangelo: High-Fidelity Neural Surface Reconstruction

Project: Neuralangelo: High-Fidelity Neural Surface Reconstruction

Abstract

Neural surface reconstruction has 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. Our approach is enabled by two key ingredients: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarseto-fine optimization on the hash grids controlling different levels of details. Even without auxiliary depth, Neuralangelo can effectively recover dense 3D surface structures from multi-view images with a fidelity that significantly surpasses previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.