## Decomposing NeRF for Editing via Feature Field Distillation

Paper Supplement

### Abstract

Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations. However, editing a scene represented by a NeRF is challenging, as the underlying connectionist representations such as MLPs or voxel grids are not object-centric or compositional. In particular, it has been difficult to selectively edit specific regions or objects. In this work, we tackle the problem of semantic scene decomposition of NeRFs to enable query-based local editing of the represented 3D scenes. We propose to distill the knowledge of off-the-shelf, self-supervised 2D image feature extractors such as CLIP-LSeg or DINO into a 3D feature field optimized in parallel to the radiance field. Given a user-specified query of various modalities such as text, an image patch, or a point-and-click selection, 3D feature fields semantically decompose 3D space without the need for re-training, and enables us to semantically select and edit regions in the radiance field. Our experiments validate that the distilled feature fields can transfer recent progress in 2D vision and language foundation models to 3D scene representations, enabling convincing 3D segmentation and selective editing of emerging neural graphics representations.
TL;DR Neural radiance fields can be edited via decomposition with arbitrary queries and feature fields distilled from pre-trained vision models.

## Results

### Query-based Scene Decomposition

Our method enables NeRF to decompose a specific object with a query like "flower."

##### Raw rendering

##### Extraction

##### Deletion

### Localizing CLIPNeRF via Scene Decomposition

CLIPNeRF optimizes a NeRF scene with a text prompt. However, naive CLIPNeRF poisons unintentional parts. We can combine our decomposition method with CLIPNeRF and selectively optimize the target object.

##### naive CLIPNeRF

##### + Our method

##### white flower

##### yellow flower

##### sunflower

##### rainbow flower

##### petunia

### Other Editings

##### move and deform apple

##### (LSeg field)

##### warp horns

##### (DINO field)

##### colorize

##### light

##### chair

##### television

##### floor

##### delete

(Note that the background behind the deleted objects can be noisy or have a hole because it lacks observation.)

##### light

##### chair

##### television

##### floor

### Other DINO Feature Fields

We visualize feature fields distilled from DINO as a techer network by PCA. Scenes are from LLFF, shiny dataset, and our dataset.