Neuralangelo: High-Fidelity Neural Surface Reconstruction
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.
647ec1f19bb822b5cd43c957
Zhaoshuo Li
claimed_verified
2023-06-06T12:32:39.807Z
647f13d85e1bc4753743f79e
/avatars/13a2a7fbd55d2e089fcf9ff0f9a41635.svg
Zhaoshuo Li
user
mli0603
647ec1f19bb822b5cd43c958
Thomas Müller
claimed_verified
2023-06-06T12:32:19.174Z
647ef81635bc6d6aa5f93819
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/qXrAoX_wCrtIUfSCfwgSq.jpeg
Thomas Müller
user
Tom94
647ec1f19bb822b5cd43c959
Alex Evans
647ec1f19bb822b5cd43c95a
Russell H. Taylor
647ec1f19bb822b5cd43c95b
Mathias Unberath
claimed_verified
2023-06-06T12:32:24.563Z
647f215345baf21ad708cf7d
/avatars/9a028ef6cac40956f357b4a0481fc1d7.svg
Mathias Unberath
user
mathiasunberath
647ec1f19bb822b5cd43c95c
Ming-Yu Liu
647ec1f19bb822b5cd43c95d
Chen-Hsuan Lin
claimed_verified
2023-06-07T19:40:56.149Z
6324e100fee2904e370bc940
/avatars/4bb215bd711452ee6d28894706773009.svg
Chen-Hsuan Lin
user
chenhsuanlin
Neuralangelo: High-Fidelity Neural Surface Reconstruction
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.
Neuralangelo combines multi-resolution 3D hash grids with neural surface rendering for detailed 3D surface reconstruction from multi-view images without auxiliary inputs.
multi-resolution 3D hash grids neural surface rendering numerical gradients coarse-to-fine optimization neuralangelo detailed 3D surface reconstruction RGB video captures