Deep learning-solutions for hand-object 3D pose and shape estimation are now very
effective when an annotated dataset is available to train them to handle the scenarios and …

Depth map quality is an important factor that affects the quality of synthesized stereoscopic
images in stereoscopic visual communication systems using the depth image-based …

A novel method for noise removal in multicamera depth sensing systems is proposed in this
work. The method uses a combination of convolutional neural networks applied on each …

We propose a generic depth-refinement scheme based on GeoNet, a recent deep-learning
approach for predicting depth and normals from a single color image, and extend it to be …

Accurate recovery of complex surfaces with dense points and normals often requires
informative measurement data, posing a great challenge for measurement. In this paper, a …

Accurate three-dimensional perception is a fundamental task in several computer vision
applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted …

Denoising of CT scans has attracted the attention of many researchers in the medical image
analysis domain. Encoder–decoder networks are deep learning neural networks that have …

Consumer-level depth cameras and depth sensors embedded in mobile devices enable
numerous applications, such as AR games and face identification. However, the quality of …

Depth perception is considered an invaluable source of information in the context of 3D
mapping and various robotics applications. However, point cloud maps acquired using …

Future generations of wireless networks face great expectations in terms of network ca-
pacity, system throughput, user density, all on a tight energy budget. In order to reach such …