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license: cc-by-nc-4.0
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# ReLaGS: Relational Language Gaussian Splatting

This repository contains the official Hugging Face model release for **ReLaGS (CVPR 2026)**.

Project page: https://dfki-av.github.io/ReLaGS/

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## Overview

**ReLaGS** is a framework for **open-vocabulary 3D scene understanding** built on top of Gaussian Splatting reconstructions.

It combines:
- A **Graph Neural Network (GNN)** trained on **3RScan** for predicting open-vocabulary relations in 3D scene graphs
- A set of **Gaussian Splatting reconstructed scenes** enriched with **open-vocabulary semantic features**

The release is intended for **evaluation and reproducibility** of open-vocabulary scene graph reasoning in reconstructed 3D scenes.


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## Repository Structure


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## 1. GNN Model (`GNN_model/`)

This directory contains a pretrained **Graph Neural Network** for predicting **open-vocabulary relations** in 3D scene graphs.

### Training data:
- 3RScan dataset

### Task:
Given a scene graph with object-level nodes, predict:
- Pairwise relations between objects
- Open-vocabulary relation labels (language-based)

### Inputs:
- Node features (geometry + appearance + learned embeddings)
- Scene graph structure (nodes + edges)

### Outputs:
- Directed edges with predicted relation labels

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## 2. Scenes (`scenes/`)

This directory contains reconstructed **Gaussian Splatting scenes** (based on LeRF-style reconstructions) augmented with semantic features.


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## Citation

If you find ReLaGS useful for your research, please cite:

```bibtex
@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 = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}
```