NeuralEditor: Editing Neural Radiance Fields via Manipulating Point
  Clouds
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.
645488c9ec40bbbd01257315
Jun-Kun Chen
claimed_verified
2023-05-25T15:21:06.245Z
6441daf6666947ad56171aef
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/lnomnCKDw6KgmbhqnskyG.jpeg
Junkun Chen
user
immortalCO
645488c9ec40bbbd01257316
Jipeng Lyu
claimed_verified
2023-05-30T09:01:35.518Z
6475b69ef60449361e556b78
https://cdn-avatars.huggingface.co/v1/production/uploads/6475b69ef60449361e556b78/qkyWjWe7y1LruNm-uiFhm.png
Jipeng Lyu
user
kedaxiaoqiu
645488c9ec40bbbd01257317
Yu-Xiong Wang
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point
  Clouds
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.
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.
Neural radiance fields NeRFs point cloud representation deterministic integration K-D tree density-adaptive voxels shape editing scene morphing zero-shot inference fine-tuning