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arXiv · 2610.33868

RelateGS: Reliable Semantic and Relational Evidence for 3D Scene Understanding with Gaussian Splatting

Seongjun Choi·Sangyoun Lee
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Abstract

Language-aware 3D Gaussian Splatting (3DGS) has advanced language-driven 3D scene understanding by incorporating semantic and relational cues into reconstructed scenes. However, relation-aware grounding depends not only on representing such information, but also on whether the underlying scene-side evidence is reliable and sufficient. Multi-view semantic aggregation can incorporate weak observations, while object relations can remain under-observed along the available camera trajectory, leading to ambiguous evidence for target disambiguation. We present RelateGS, a structured language-aware Gaussian representation that selectively refines semantic and relational evidence for 3D visual grounding. RelateGS suppresses unreliable observations during semantic feature uplifting and consolidates multi-view object observations into a persistent 3D graph. The graph further identifies under-observed object relations and guides targeted re-querying of informative existing frames to complement their evidence. Experiments on Sr3D+ and Nr3D demonstrate competitive 3D grounding performance, improving the previous best by 7.9 and 1.7 percentage points at Acc@0.25, respectively.

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Seongjun Choi, Sangyoun Lee. 2026-10-24. RelateGS: Reliable Semantic and Relational Evidence for 3D Scene Understanding with Gaussian Splatting. https://arxiv.org/abs/2610.33868

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