GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024

We present GFreeDet, an unseen object detection approach that leverages Gaussian splatting and vision Foundation models under model-free setting. Unlike existing methods that rely on predefined CAD templates, GFreeDet reconstructs objects directly from reference videos using Gaussian splatting, enabling robust detection of novel objects without prior 3D models. Evaluated on the BOP-H3 benchmark, GFreeDet achieves comparable performance to CAD-based methods, demonstrating the viability of model-free detection for mixed reality (MR) applications. Notably, GFreeDet won the best overall method and the best fast method awards in the model-free 2D detection track at BOP Challenge 2024.

@article{liu2025_2412.01552,
  title={ GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024 },
  author={ Xingyu Liu and Gu Wang and Chengxi Li and Yingyue Li and Chenyangguang Zhang and Ziqin Huang and Xiangyang Ji },
  journal={arXiv preprint arXiv:2412.01552},
  year={ 2025 }
}