Recently, 3D Gaussian Splatting (3DGS) has achieved significant performance on indoor surface reconstruction and open-vocabulary segmentation. This paper presents GLS, a unified framework of surface reconstruction and open-vocabulary segmentation based on 3DGS. GLS extends two fields by exploring the correlation between them. For indoor surface reconstruction, we introduce surface normal prior as a geometric cue to guide the rendered normal, and use the normal error to optimize the rendered depth. For open-vocabulary segmentation, we employ 2D CLIP features to guide instance features and utilize DEVA masks to enhance their view consistency. Extensive experiments demonstrate the effectiveness of jointly optimizing surface reconstruction and open-vocabulary segmentation, where GLS surpasses state-of-the-art approaches of each task on MuSHRoom, ScanNet++, and LERF-OVS datasets. Code will be available at this https URL.
近年来，3D 高斯投影（3D Gaussian Splatting, 3DGS）在室内表面重建和开放词汇分割任务中取得了显著的性能进展。本文提出了 GLS，一个基于 3DGS 的统一框架，用于表面重建和开放词汇分割。GLS 通过探索两者之间的关联，扩展了这两个领域。 在室内表面重建方面，我们引入了表面法线先验作为几何线索来引导渲染的法线，并通过法线误差优化渲染的深度。在开放词汇分割方面，我们采用 2D CLIP 特征来引导实例特征，并利用 DEVA 掩码增强视图一致性。 大量实验表明，同时优化表面重建和开放词汇分割的有效性。在 MuSHRoom、ScanNet++ 和 LERF-OVS 数据集上，GLS 在每项任务中均超越了当前最先进的方法，展示了卓越的性能和通用性。