Get 3D surface from Neural Radiance Field using signed surface approximation with NeRFMeshing
Get 3D surface from Neural Radiance Field using signed surface approximation with NeRFMeshing
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
arXiv paper abstract https://arxiv.org/abs/2303.09431
arXiv PDF paper https://arxiv.org/pdf/2303.09431.pdf
… Neural Radiance Fields (NeRFs) … proposes that each 3D point can emit radiance, allowing to conduct view synthesis using differentiable volumetric rendering.
While neural radiance fields can … represent 3D scenes for … image rendering, 3D meshes are still the main scene representation supported by most computer graphics and simulation pipelines
… Obtaining 3D meshes from neural radiance fields … an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.
… propose a novel compact and flexible architecture that enables easy 3D surface reconstruction from any NeRF-driven approach.
… having trained the radiance field, … distill the volumetric 3D representation into a Signed Surface Approximation Network, allowing easy extraction of the 3D mesh and appearance.
… final 3D mesh is physically accurate and can be rendered in real time …
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