Tackling View-Dependent Semantics in 3D Language Gaussian Splatting

• URL: http://arxiv.org/abs/2505.24746v1
• Date: Fri, 30 May 2025 16:06:32 GMT
• Title: Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
• Authors: Jiazhong Cen, Xudong Zhou, Jiemin Fang, Changsong Wen, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian,
• Abstract summary: LaGa establishes cross-view semantic connections by decomposing the 3D scene into objects.<n>It constructs view-aggregated semantic representations by clustering semantic descriptors and reweighting them based on multi-view semantics.<n>Under the same settings, LaGa achieves a significant improvement of +18.7% mIoU over the previous SOTA on the LERF-OVS dataset.
• Score: 80.88015191411714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in 3D Gaussian Splatting (3D-GS) enable high-quality 3D scene reconstruction from RGB images. Many studies extend this paradigm for language-driven open-vocabulary scene understanding. However, most of them simply project 2D semantic features onto 3D Gaussians and overlook a fundamental gap between 2D and 3D understanding: a 3D object may exhibit various semantics from different viewpoints--a phenomenon we term view-dependent semantics. To address this challenge, we propose LaGa (Language Gaussians), which establishes cross-view semantic connections by decomposing the 3D scene into objects. Then, it constructs view-aggregated semantic representations by clustering semantic descriptors and reweighting them based on multi-view semantics. Extensive experiments demonstrate that LaGa effectively captures key information from view-dependent semantics, enabling a more comprehensive understanding of 3D scenes. Notably, under the same settings, LaGa achieves a significant improvement of +18.7% mIoU over the previous SOTA on the LERF-OVS dataset. Our code is available at: https://github.com/SJTU-DeepVisionLab/LaGa.

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