3D Gaussian Splatting has emerged as a powerful 3D scene representation technique, capturing fine details with high efficiency. In this paper, we introduce a novel voting-based method that extends 2D segmentation models to 3D Gaussian splats. Our approach leverages masked gradients, where gradients are filtered by input 2D masks, and these gradients are used as votes to achieve accurate segmentation. As a byproduct, we discovered that inference-time gradients can also be used to prune Gaussians, resulting in up to 21% compression. Additionally, we explore few-shot affordance transfer, allowing annotations from 2D images to be effectively transferred onto 3D Gaussian splats. The robust yet straightforward mathematical formulation underlying this approach makes it a highly effective tool for numerous downstream applications, such as augmented reality (AR), object editing, and robotics. The project code and additional resources are available at this https URL.
3D Gaussian Splatting 已成为一种强大的 3D 场景表示技术，能够高效捕捉精细细节。在本文中，我们提出了一种基于投票的创新方法，将 2D 分割模型扩展到 3D 高斯散点。我们的方法利用了掩码梯度，通过输入的 2D 掩码过滤梯度，并将这些梯度作为投票，以实现精确的分割。作为副产品，我们发现推理时的梯度还可以用于剪枝高斯分布，压缩率可达 21%。此外，我们还探索了少样本可供性迁移，使 2D 图像的标注能够有效迁移到 3D 高斯散点上。该方法背后坚实且简洁的数学公式，使其成为增强现实 (AR)、物体编辑、机器人等多个下游应用的有效工具。