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.

本研究解决了NeRF在3D场景建模中难以进行语义分割的问题。提出了一种稳健的方法，通过对抗竞争对象槽来匹配掩膜，从而有效分解场景，生成高质量的3D全景分割。这一方法展示了在复杂场景中提取3D资产的能力，具有广泛的应用潜力。

DiscoNeRF：无类别对象场用于3D对象发现