SANeRF-HQ: Segment Anything for NeRF in High Quality
Authors: name: Yichen Liu, name: Benran Hu, name: Chi-Keung Tang, name: Yu-Wing Tai

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 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 quantitatively on
multiple NeRF datasets where high-quality ground-truths are available or
manually annotated. SANeRF-HQ shows a significant quality improvement over
previous state-of-the-art methods in NeRF object segmentation, provides higher
flexibility for object localization, and enables more consistent object
segmentation across multiple views. Additional information can be found at
https://lyclyc52.github.io/SANeRF-HQ/.