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