  Recently, the Segment Anything Model (SAM) has showcased remarkable
capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has
gained popularity as a method for various 3D problems beyond novel view
synthesis. Though there exist initial attempts to incorporate these two methods
into 3D segmentation, they face the challenge of accurately and consistently
segmenting objects in complex scenarios. In this paper, we introduce the
Segment Anything for NeRF in High Quality (SANeRF-HQ) to achieve high-quality
3D segmentation of any target object in a given scene. SANeRF-HQ utilizes SAM
for open-world object segmentation guided by user-supplied prompts, while
leveraging NeRF to aggregate information from different viewpoints. To overcome
the aforementioned challenges, we employ density field and RGB similarity to
enhance the accuracy of segmentation boundary during the aggregation.
Emphasizing on segmentation accuracy, we evaluate our method on multiple NeRF
datasets where high-quality ground-truths are available or manually annotated.
SANeRF-HQ shows a significant quality improvement over state-of-the-art methods
in NeRF object segmentation, provides higher flexibility for object
localization, and enables more consistent object segmentation across multiple
views. Results and code are available at the project site:
https://lyclyc52.github.io/SANeRF-HQ/.
