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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