Neuralangelo: High-Fidelity Neural Surface Reconstruction

• URL: http://arxiv.org/abs/2306.03092v2
• Date: Mon, 12 Jun 2023 20:50:07 GMT
• Title: Neuralangelo: High-Fidelity Neural Surface Reconstruction
• Authors: Zhaoshuo Li, Thomas M\"uller, Alex Evans, Russell H. Taylor, Mathias Unberath, Ming-Yu Liu, Chen-Hsuan Lin
• Abstract summary: We present Neuralangelo, which combines the representation power of multi-resolution 3D hash grids with neural surface rendering. 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.
• Score: 22.971952498343942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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