Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to meet the real-time requirements of low-power robotic platforms. Additionally, most methods are designed for opaque objects and struggle with transparent objects due to the special properties of reflection and refraction. To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation. To leverage local information and avoid overfitting issues when integrating it with global information, we design a new fusion branch and shortcuts to exploit low-level features and a loss function to suppress overfitting. This results in an accurate and user-friendly depth rectification framework which can recover dense depth estimation from RGB-D images alone. Extensive experiments demonstrate that FDCT can run about 70 FPS with a higher accuracy than the state-of-the-art methods. We also demonstrate that FDCT can improve pose estimation in object grasping tasks. The source code is available at https://github.com/Nonmy/FDCT

翻译：深度补全对于自动驾驶、三维重建及操纵等许多机器人任务至关重要。尽管已取得显著进展，现有方法仍计算密集，且常常无法满足低功耗机器人平台的实时要求。此外，大多数方法专为不透明物体设计，由于反射和折射的特殊性质，难以处理透明物体。为应对这些挑战，我们提出了一种面向透明物体的快速深度补全框架（FDCT），该框架亦有益于下游任务，如物体姿态估计。为利用局部信息并避免在与全局信息融合时的过拟合问题，我们设计了一种新的融合分支和捷径来挖掘低层特征，并设计了一种损失函数以抑制过拟合。由此产生了一个准确且用户友好的深度修正框架，该框架可仅从RGB-D图像中恢复稠密深度估计。大量实验表明，FDCT能以约70 FPS的速度运行，且精度高于最先进的方法。我们还证明，FDCT能够改善物体抓取任务中的姿态估计。源代码可在https://github.com/Nonmy/FDCT获取。