title: NeuralEditor: Editing Neural Radiance Fields via Manipulating Point
    Clouds
summary: This paper proposes NeuralEditor that enables neural radiance fields (NeRFs)
  natively editable for general shape editing tasks. Despite their impressive
  results on novel-view synthesis, it remains a fundamental challenge for NeRFs
  to edit the shape of the scene. Our key insight is to exploit the explicit
  point cloud representation as the underlying structure to construct NeRFs,
  inspired by the intuitive interpretation of NeRF rendering as a process that
  projects or "plots" the associated 3D point cloud to a 2D image plane. To this
  end, NeuralEditor introduces a novel rendering scheme based on deterministic
  integration within K-D tree-guided density-adaptive voxels, which produces both
  high-quality rendering results and precise point clouds through optimization.
  NeuralEditor then performs shape editing via mapping associated points between
  point clouds. Extensive evaluation shows that NeuralEditor achieves
  state-of-the-art performance in both shape deformation and scene morphing
  tasks. Notably, NeuralEditor supports both zero-shot inference and further
  fine-tuning over the edited scene. Our code, benchmark, and demo video are
  available at https://immortalco.github.io/NeuralEditor.
paper_authors._id: 645488c9ec40bbbd01257315
paper_authors.name: Jun-Kun Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-25T15:21:06.245Z
paper_authors._id: 6441daf6666947ad56171aef
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/lnomnCKDw6KgmbhqnskyG.jpeg
paper_authors.fullname: Junkun Chen
paper_authors.type: user
paper_authors.user: immortalCO
paper_authors._id: 645488c9ec40bbbd01257316
paper_authors.name: Jipeng Lyu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-30T09:01:35.518Z
paper_authors._id: 6475b69ef60449361e556b78
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6475b69ef60449361e556b78/qkyWjWe7y1LruNm-uiFhm.png
paper_authors.fullname: Jipeng Lyu
paper_authors.type: user
paper_authors.user: kedaxiaoqiu
paper_authors._id: 645488c9ec40bbbd01257317
paper_authors.name: Yu-Xiong Wang
paper_title: NeuralEditor: Editing Neural Radiance Fields via Manipulating Point
    Clouds
paper_summary: This paper proposes NeuralEditor that enables neural radiance fields (NeRFs)
  natively editable for general shape editing tasks. Despite their impressive
  results on novel-view synthesis, it remains a fundamental challenge for NeRFs
  to edit the shape of the scene. Our key insight is to exploit the explicit
  point cloud representation as the underlying structure to construct NeRFs,
  inspired by the intuitive interpretation of NeRF rendering as a process that
  projects or "plots" the associated 3D point cloud to a 2D image plane. To this
  end, NeuralEditor introduces a novel rendering scheme based on deterministic
  integration within K-D tree-guided density-adaptive voxels, which produces both
  high-quality rendering results and precise point clouds through optimization.
  NeuralEditor then performs shape editing via mapping associated points between
  point clouds. Extensive evaluation shows that NeuralEditor achieves
  state-of-the-art performance in both shape deformation and scene morphing
  tasks. Notably, NeuralEditor supports both zero-shot inference and further
  fine-tuning over the edited scene. Our code, benchmark, and demo video are
  available at https://immortalco.github.io/NeuralEditor.
paper_ai_summary: NeuralEditor introduces a novel rendering scheme for NeRFs using point clouds and K-D tree-guided density-adaptive voxels, enabling state-of-the-art shape editing and scene morphing with support for zero-shot inference and fine-tuning.
paper_ai_keywords: Neural radiance fields NeRFs point cloud representation deterministic integration K-D tree density-adaptive voxels shape editing scene morphing zero-shot inference fine-tuning