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 this https URL

提出了一种适用于透明物体的快速深度补全框架（FDCT），通过利用局部信息和设计新的融合分支和捷径，以及使用损失函数来抑制过拟合问题，实现了准确且易于使用的深度矫正框架。该框架可以仅通过 RGB-D 图像恢复稠密深度估计，运行速度约为 70 FPS，优于现有方法，同时还能提升物体抓取任务中的姿态估计性能。