DiscoNeRF: Class-Agnostic Object Field for 3D Object Discovery

Neural Radiance Fields (NeRFs) have become a powerful tool for modeling 3D scenes from multiple images.

However, NeRFs remain difficult to segment into semantically meaningful regions.

Previous approaches to 3D segmentation of NeRFs either require user interaction to isolate a single object, or they rely on 2D semantic masks with a limited number of classes for supervision.

As a consequence, they generalize poorly to class-agnostic masks automatically generated in real scenes.

This is attributable to the ambiguity arising from zero-shot segmentation, yielding inconsistent masks across views.

In contrast, we propose a method that is robust to inconsistent segmentations and successfully decomposes the scene into a set of objects of any class.

By introducing a limited number of competing object slots against which masks are matched, a meaningful object representation emerges that best explains the 2D supervision and minimizes an additional regularization term.

Our experiments demonstrate the ability of our method to generate 3D panoptic segmentations on complex scenes, and extract high-quality 3D assets from NeRFs that can then be used in virtual 3D environments.