基于2D分割引导的3D高斯泼溅分割OA

3D Gaussian Splatting,as an emerging 3D representation method,has demonstrated remarkable performance in complex scene reconstruction and rendering.In the context of 3D Gaussian Splatting object segmentation,most existing methods require retraining a dedicated segmentation property within the 3D Gaussian Splatting representation.This process is not only time-consuming but can also lead to ambiguous segmentation results,particularly in boundary regions.To address these issues,an efficient 3D segmentation method guided by 2D features is proposed.This method correlates 2D visual information with 3D spatial information to segment target objects re-presented by 3D Gaussians using 2D prompts.The entire process eliminates the need for a second iterative gradient descent to add new segmentation properties to each Gaussian.Specifically,it first assigns initial segmentation labels to the 3D Gaussians based on 2D masks.Subsequently,a neighboring boundary refinement algorithm is employed to refine the boundaries of the target objects,successfully overcoming the challenge of boundary blurring and significantly improving segmentation accuracy.Experimental results show that the proposed method achieves high-quality 3D segmentation,reduces processing time by over80％,and significantly enhances segmentation accuracy,with mIoU increasing by more than1 percentage point and mACC increasing by0.3 percentage points.