# GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024 Source: [GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:34:18.624Z Authors: Xingyu Liu, Gu Wang, Chengxi Li, Yingyue Li, Chenyangguang Zhang, Ziqin Huang, Xiangyang Ji Published: 2024-12-02T14:38:26.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on HOPE BOP v2 (test)](): Object Detection; HOPE BOP v2 (test); AP: 38.4. - [Object Detection on HANDAL BOP (test)](): Object Detection; HANDAL BOP (test); AP: 26.4. ## Original sources and implementation [Original paper]() [PDF]()