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.

Due to the complexity of the underwater environment, underwater images captured by optical cameras usually suffer from haze and color distortion. Based on the similarity between the underwater imaging model and the atmosphere model, the dehazing algorithm is widely adopted for underwater image enhancement. As a key factor of the dehazing model, background light directly affects the quality of image enhancement. This paper proposes a novel background light estimation method which can enhance the underwater image. And it can be applied in 30-60m depth with artificial light. The method combines deep learning to obtain red channel information of the background light in the dark channel of the underwater image. Then, the background light is obtained by adaptive color deviation correction. Finally, the experiments of underwater images enhancement are carried out, using the dark channel prior algorithm based on the proposed background light estimation method. The results show that the proposed method effectively improves underwater image blur and color deviation, and is superior to other methods in multiple non-reference image evaluation indicators.

Selective laser sintering (SLS) is an additive manufacturing technology that can work with a variety of metal materials, and has been widely employed in many applications. The establishment of a data correlation model through the analysis of temperature field images is a recognized research method to realize the monitoring and quality control of the SLS process. In this paper, the key features of the temperature field in the process are extracted from three levels, and the mathematical model and data structure of the key features are constructed. Feature extraction, dimensional reduction, and parameter optimization are realized based on principal component analysis (PCA) and support vector machine (SVM), and the prediction model is built and optimized. Finally, the feasibility of the proposed algorithms and model is verified by experiments.