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.

翻译：本文提出SAGA（Segment Any 3D GAussians），一种基于三维高斯溅射（3D-GS）的高效三维可提示分割方法。给定二维视觉提示作为输入，SAGA能在4毫秒内分割出由三维高斯模型表示的对应三维目标。这是通过为每个三维高斯模型附加尺度门控亲和特征实现的，该特征赋予模型面向多粒度分割的新特性。具体而言，本文提出一种尺度感知对比训练策略用于尺度门控亲和特征学习：1）将Segment Anything Model（SAM）的分割能力从二维掩码蒸馏至亲和特征；2）采用软尺度门控机制，通过根据指定的三维物理尺度调整各特征通道的幅度，以处理三维分割中的多粒度模糊性问题。评估结果表明，SAGA在实现实时多粒度分割的同时，其分割质量可与最先进方法相媲美。作为首批解决3D-GS中可提示分割问题的方法之一，SAGA的简洁性与有效性为该领域的未来发展奠定了基础。我们的代码将公开释放。