TDCNet: Transparent Objects Depth Completion With CNN-Transformer Dual-Branch Parallel Network

The perception and processing of transparent objects face significant challenges in various applications, primarily due to the limitations of traditional sensors. These sensors often struggle to capture the complete depth information of transparent objects, mainly due to the refraction and reflection of light on their surfaces, as well as the lack of visible texture. Previous research has explored the use of deep learning models to generate complete depth maps from RGB images and corrupted depth data obtained from depth sensors. However, existing methods suffer from design flaws that limit the effectiveness of depth completion. To address this issue, we propose TDCNet, a novel dual-branch CNN-Transformer parallel network specifically designed for depth completion of transparent objects. Our framework consists of two distinct branches: one focuses on feature extraction from partial depth maps, while the other processes RGB-D images. Experimental results demonstrate that our model achieves state-of-the-art performance on multiple public datasets. The code and pre-trained models are publicly available at https://github.com/XianghuiFan/TDCNet.