NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds

Understand

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.