  This paper presents SAGA (Segment Any 3D GAussians), a highly efficient 3D
promptable segmentation method based on 3D Gaussian Splatting (3D-GS). Given 2D
visual prompts as input, SAGA can segment the corresponding 3D target
represented by 3D Gaussians within 4 ms. This is achieved by attaching an
scale-gated affinity feature to each 3D Gaussian to endow it a new property
towards multi-granularity segmentation. Specifically, a scale-aware contrastive
training strategy is proposed for the scale-gated affinity feature learning. It
1) distills the segmentation capability of the Segment Anything Model (SAM)
from 2D masks into the affinity features and 2) employs a soft scale gate
mechanism to deal with multi-granularity ambiguity in 3D segmentation through
adjusting the magnitude of each feature channel according to a specified 3D
physical scale. Evaluations demonstrate that SAGA achieves real-time
multi-granularity segmentation with quality comparable to state-of-the-art
methods. As one of the first methods addressing promptable segmentation in
3D-GS, the simplicity and effectiveness of SAGA pave the way for future
advancements in this field. Our code will be released.
