4 papers
Gaussian Belief Propagation Network for Depth Completion
Jie Tang, Pingping Xie, Jian Li +1
Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning methods have achieved state-of-the-art (SOTA), effectiv…
Bilateral Propagation Network for Depth Completion
Jie Tang, Fei-Peng Tian, Boshi An +2
Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly propagat…
Design of Novel Loss Functions for Deep Learning in X-ray CT
Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare +4
Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of dat…
Learning Guided Convolutional Network for Depth Completion
Jie Tang, Fei-Peng Tian, Wei Feng +2
Dense depth perception is critical for autonomous driving and other robotics applications. However, modern LiDAR sensors only provide sparse depth measurement. It is thus necessary…