onandon/EmbodiedSplat

<p align="center"> <h1 align="center">EmbodiedSplat 🛋️<br> Online Feed-Forward Semantic 3DGS <br> for Open-Vocabulary 3D Scene Understanding</h1> <p align="center"> <a href="https://www.linkedin.com/in/seungjun-lee-43101a261/">Seungjun Lee</a></span> · <a href="https://scholar.google.com/citations?user=7rf6Bw4AAAAJ&hl=en">Zihan Wang</a></span> · <a href="https://wangys16.github.io">Yunsong Wang</a></span> · <a href="https://www.comp.nus.edu.sg/~leegh/">Gim Hee Lee</a> <br> National University of Singapore<br> </p> <h2 align="center">CVPR 2026</h2> <h3 align="center"><a href="https://github.com/0nandon/EmbodiedSplat">Code</a> | <a href="https://arxiv.org/pdf/2603.04254">Paper</a> | <a href="https://0nandon.github.io/EmbodiedSplat/">Project Page</a> </h3> <div align="center"> <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> <a href="https://pytorchlightning.ai/"><img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a> </div> </p>

<p align="center"> <a href=""> <img src="teaser.png" alt="Logo" width="100%"> </a> </p> <p align="center"> <strong>Build and understand at Once!</strong> By taking over 300 streaming images, our <strong>EmbodiedSplat</strong> reconstructs whole-scene open-vocabulary 3DGS in online manner at up to 5-6 FPS per-frame processing time. Reconstructed scene supports diverse perception tasks such as open-vocabulary 3D semantic segmentation, 2D-rendered semantic segmentation and novel-view color synthesis with depth rendering. </p>

Citation

If you find our code or paper useful, please cite

@article{lee2026embodiedsplat,
  title={EmbodiedSplat: Online Feed-Forward Semantic 3DGS for Open-Vocabulary 3D Scene Understanding},
  author={Lee, Seungjun and Wang, Zihan and Wang, Yunsong and Lee, Gim Hee},
  journal={arXiv preprint arXiv:2603.04254},
  year={2026}
}