3D open-vocabulary scene understanding, crucial for advancing augmented reality and robotic applications, involves interpreting and locating specific regions within a 3D space as directed by natural language instructions. To this end, we introduce GOI, a framework that integrates semantic features from 2D vision-language foundation models into 3D Gaussian Splatting (3DGS) and identifies 3D Gaussians of Interest using an Optimizable Semantic-space Hyperplane. Our approach includes an efficient compression method that utilizes scene priors to condense noisy high-dimensional semantic features into compact low-dimensional vectors, which are subsequently embedded in 3DGS. During the open-vocabulary querying process, we adopt a distinct approach compared to existing methods, which depend on a manually set fixed empirical threshold to select regions based on their semantic feature distance to the query text embedding. This traditional approach often lacks universal accuracy, leading to challenges in precisely identifying specific target areas. Instead, our method treats the feature selection process as a hyperplane division within the feature space, retaining only those features that are highly relevant to the query. We leverage off-the-shelf 2D Referring Expression Segmentation (RES) models to fine-tune the semantic-space hyperplane, enabling a more precise distinction between target regions and others. This fine-tuning substantially improves the accuracy of open-vocabulary queries, ensuring the precise localization of pertinent 3D Gaussians. Extensive experiments demonstrate GOI's superiority over previous state-of-the-art methods. Our project page is available at https://goi-hyperplane.github.io/ .

通过在 3D 空间中解释和定位特定区域，GOI 整合了来自 2D 视觉-语言基础模型的语义特征，利用可优化的语义空间超平面识别感兴趣的 3D 高斯。通过在特性空间内进行超平面划分的特征选择过程，GOI 方法通过微调语义空间超平面，实现了开放式词汇查询的精准定位。大量实验证明了 GOI 方法相对于之前的最先进方法的优越性。

使用可优化的开放词汇语义空间超平面找到感兴趣的三维高斯函数