HDCNet: A Hybrid Depth Completion Network for Grasping Transparent and Reflective Objects

Abstract

Depth perception of transparent and reflective objects has long been a critical challenge in robotic manipulation. Conventional depth sensors often fail to provide reliable measurements on such surfaces, limiting the performance of robots in perception and grasping tasks. To address this issue, we propose a novel depth completion network, HDCNet, which integrates the complementary strengths of transformer, convolutional neural network (CNN), and Mamba architectures. Specifically, the encoder is designed as a dual-branch Transformer-CNN framework to extract modality-specific features. At the shallow layers of the encoder, we introduce a lightweight multimodal fusion module to effectively integrate low-level features. At the network bottleneck, a Transformer–Mamba hybrid fusion module is developed to achieve deep integration of high-level semantic and global contextual information, significantly enhancing depth completion accuracy and robustness. Extensive evaluations on multiple public datasets demonstrate that HDCNet achieves leading performance in depth completion tasks, surpassing many state-of-the-art methods. Furthermore, robotic grasping experiments show that HDCNet substantially improves grasp success rates for transparent and reflective objects, achieving a 60-percentage-point improvement.