SFL-GS: Spatio-Temporal Feature-guided Learning for 3D Gaussian Segmentation

3D Gaussian splatting has emerged as a promising technique for real-time scene representation, making interactive 3D segmentation increasingly important in scene manipulation. However, inconsistent results generated by 2D segmentation across different viewpoints pose significant challenges for learning 3D segmentation feature fields. The accuracy of 3D segmentation decreases substantially when cross-view 2D segmentation results conflict. To address this issue, we present Spatio-Temporal Feature-guided Learning for 3D Gaussian Segmentation (SFL-GS), an efficient interactive 3D segmentation framework. SFL-GS employs a novel Spatio-temporal Feature-guided Learning (SFL) strategy to capture spatio-temporally consistent features and masks from 2D segmentation results across different views, thereby coherently guiding the learning of 3D segmentation feature fields. To refine feature and mask consistency in complex scenes, particularly under severe occlusion, our framework develops an enhanced optimization strategy that integrates statistical filtering, dynamic scale growth, and edge-aware optimization. This approach results in clearer boundaries and significantly improves segmentation accuracy, even in challenging environments. Extensive experiments demonstrate that our method achieves superior accuracy in segmentation tasks, making it suitable for precise and efficient 3D segmentation requirements in real-world applications.