Neuralangelo: High-Fidelity Neural Surface Reconstruction
This addresses the challenge of high-fidelity large-scale scene reconstruction from RGB video for applications in computer vision and graphics.
The paper tackles the problem of recovering detailed 3D surface structures from multi-view images, presenting Neuralangelo which combines multi-resolution 3D hash grids with neural surface rendering and achieves fidelity significantly surpassing previous methods.
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.