GLS：几何感知的3D语言高斯泼溅
GLS: Geometry-aware 3D Language Gaussian Splatting
AI总结：
本文提出基于3DGS的统一框架GLS，通过引入法线先验优化表面重建，并利用CLIP特征和DEVA掩码增强3D开放词汇分割的平滑度与视图一致性，在多个数据集上超越了现有最先进方法。
摘要：
最近，3D Gaussian Splatting (3DGS) 在室内表面重建和3D开放词汇分割上取得了令人印象深刻的性能。本文提出了GLS，一个基于3DGS的3D表面重建与开放词汇分割的统一框架。GLS通过提升两者的锐度和平滑度来扩展这两个领域。对于室内表面重建，我们引入表面法线先验作为几何线索来指导渲染法线，并使用法线误差来优化渲染深度。对于3D开放词汇分割，我们采用2D CLIP特征来指导实例特征并增强表面平滑度，然后利用DEVA掩码来维持其视图一致性。大量实验证明了联合优化表面重建和3D开放词汇分割的有效性，其中GLS在MuSHRoom、ScanNet++和LERF-OVS数据集上超越了每项任务的现有最先进方法。
英文摘要：
Recently, 3D Gaussian Splatting (3DGS) has achieved impressive performance on indoor surface reconstruction and 3D open-vocabulary segmentation. This paper presents GLS, a unified framework of 3D surface reconstruction and open-vocabulary segmentation based on 3DGS. GLS extends two fields by improving their sharpness and smoothness. 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 3D open-vocabulary segmentation, we employ 2D CLIP features to guide instance features and enhance the surface smoothness, then utilize DEVA masks to maintain their view consistency. Extensive experiments demonstrate the effectiveness of jointly optimizing surface reconstruction and 3D open-vocabulary segmentation, where GLS surpasses state-of-the-art approaches of each task on MuSHRoom, ScanNet++ and LERF-OVS datasets. Project webpage: https://jiaxiongq.github.io/GLS_ProjectPage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
