ReLaGS: Relational Language Gaussian Splatting

Abstract

Achieving unified 3D perception and reasoning across tasks such as segmentation, retrieval, and relation understanding remains challenging, as existing methods are either object-centric or rely on costly training for inter-object reasoning. We present a novel framework that constructs a hierarchical language-distilled Gaussian scene and its 3D semantic scene graph without scene-specific training. A Gaussian pruning mechanism refines scene geometry, while a robust multi-view language alignment strategy aggregates noisy 2D features into accurate 3D object embeddings. On top of this hierarchy, we build an open-vocabulary 3D scene graph with Vision Language-derived annotations and Graph Neural Network-based relational reasoning. Our approach enables efficient and scalable open-vocabulary 3D reasoning by jointly modeling hierarchical semantics and inter/intra-object relationships, validated across tasks including open-vocabulary segmentation, scene graph generation, and relation-guided retrieval.

Citation

@inproceedings{xiearafa2026relags,
  title     = {ReLaGS: Relational Language Gaussian Splatting},
  author    = {Xie, Yaxu and Arafa, Abdalla and Javanmardi, Alireza and
               Millerdurai, Christen and Hu, Jia Cheng and Wang, Shaoxiang and
               Pagani, Alain and Stricker, Didier},
  booktitle = {CVPR},
  year      = {2026}
}

Acknowledgements

This work has been partially funded by the EU projects dAIEDGE (GA Nr 101120726) and LUMINOUS (GA Nr 101135724).