The sensing and manipulation of transparent objects present a critical challenge in industrial and laboratory robotics. Conventional sensors face challenges in obtaining the full depth of transparent objects due to the refraction and reflection of light on their surfaces and their lack of visible texture. Previous research has attempted to obtain complete depth maps of transparent objects from RGB and damaged depth maps (collected by depth sensor) using deep learning models. However, existing methods fail to fully utilize the original depth map, resulting in limited accuracy for deep completion. To solve this problem, we propose TDCNet, a novel dual-branch CNN-Transformer parallel network for transparent object depth completion. The proposed framework consists of two different branches: one extracts features from partial depth maps, while the other processes RGB-D images. Experimental results demonstrate that our model achieves state-of-the-art performance across multiple public datasets. Our code and the pre-trained model are publicly available at https://github.com/XianghuiFan/TDCNet.

翻译：透明物体的感知与操作是工业和实验室机器人领域面临的关键挑战。由于光线在其表面的折射与反射以及缺乏可见纹理，传统传感器难以获取透明物体的完整深度信息。先前研究尝试利用深度学习模型，从RGB图像及受损深度图（由深度传感器采集）中获取透明物体的完整深度图。然而，现有方法未能充分利用原始深度图，导致深度补全精度受限。为解决此问题，我们提出TDCNet——一种用于透明物体深度补全的新型CNN-Transformer双分支并行网络。该框架包含两个独立分支：一支从局部深度图中提取特征，另一支处理RGB-D图像。实验结果表明，我们的模型在多个公开数据集上均达到了最先进的性能。我们的代码与预训练模型已公开于https://github.com/XianghuiFan/TDCNet。