BF‐GNet: An RGB‐D Fusion Network for Grasp Pose Estimation in Complex Background Environments

In unstructured environments, background regions and target objects often share visual attributes, such as texture, color, and structure. This similarity easily leads to feature confusion, posing significant challenges for grasp pose estimation. To address this issue, we propose BF‐GNet (RGB‐D fusion network for grasp pose estimation in complex background environments), an RGB‐D fusion network designed for grasp detection under complex backgrounds. At its core lies a scene‐aware Bimodal Foreground Enhancement Module, which employs a multilayer perceptron–based controller to adaptively weight RGB and depth modalities according to the input scene: prioritizing depth information in texture‐deficient conditions and relying more on RGB semantic cues when geometry is ambiguous. This adaptive fusion strategy effectively emphasizes salient foreground grasp regions while suppressing background interference. In addition, BF‐GNet integrates a Feature‐Channel‐Sharing 2D Selective Scan (FCSS) Module based on State Space Models, enabling long‐range dependency modeling with linear complexity. Experimental results show that BF‐GNet achieves accuracies of 98.9% and 94.8% on the Cornell and Jacquard data sets, and 95.12% and 91.8% on the cluttered CBRGD and large‐scale GraspNet‐1Billion data sets, respectively. Meanwhile, its computational cost remains as low as 0.72 GFLOPs, demonstrating strong competitiveness against existing methods. Furthermore, real‐world robotic experiments yield an average grasp success rate of 90.50%, validating the robustness, generalization capability, and practical value of BF‐GNet in complex scenarios. The video is available at https://youtube.com/shorts/eyLFwb3K-UE?si=B5U9xzXCt5YliGvB .