ADDFNet: A Robotic Grasping Depth Map Completion Network Integrating Differential Enhancement Convolution and Hybrid Attention

In the field of industrial robotic vision, accurate recognition and localization of transparent objects pose significant challenges. Unlike opaque objects, transparent objects are difficult to distinguish in RGB images, and due to refraction and reflection, their depth information often suffers from large-area missing or erroneous values, leading to failed grasp pose prediction. Therefore, depth completion is crucial for transparent object grasping tasks. However, existing depth completion methods still exhibit obvious limitations. Multi-stage optimization methods, while achieving high accuracy, involve complex pipelines and high computational costs. Single-stage end-to-end networks, when processing sparse edge features of transparent objects that are also contaminated by background interference, are constrained by the receptive field and smoothing effect of conventional convolutions, often resulting in contour blurring and loss of geometric details. Moreover, existing methods still lack sufficient capability in modeling multi-directional gradient variations of transparent objects under complex backgrounds. To address these issues, this paper proposes ADDFNet for transparent object depth completion, achieving synergistic improvement in accuracy and robustness through two key designs: MDAM and CMFR. To tackle the problem of sparse edge features of transparent objects that are easily disturbed by noise, we design the Multi-directional Differential Attention Module (MDAM), which explicitly extracts multi-directional gradient information through multi-branch differential convolution. Within MDAM, we introduce the Detail Enhancement Differential sub-Module (DEDM) and the Dynamic Convolution with Symmetry-enhanced Geometry Attention sub-module (DSCA) to enhance the network’s perception of fine contours and improve global–local synergistic modeling capability. To address insufficient cross-modal information interaction, we introduce the Cross-Modal Feature Refinement (CMFR) module, which utilizes RGB context to guide and enhance depth features layer by layer during the encoding stage, improving the accuracy and robustness of depth completion while mitigating feature degradation caused by traditional simple fusion approaches. Experimental results on the ClearPose and TransCG datasets demonstrate that ADDFNet outperforms comparison methods in terms of RMSE, REL, MAE, and threshold accuracy metrics, exhibiting more stable performance in edge recovery and internal detail reconstruction of transparent objects.