Additional layers to the U-Net architecture leads to additional parameters and network
complexity. The Visual Geometry Group (VGG) architecture with 16 backbones can …

In this work, we propose a method for estimating depth for an image of a monocular camera
in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a …

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient
(near infrared) and reflectivity imagery, as well as a sample benchmark task using depth …

Monocular depth estimation is a very valuable but also very challenging problem. In order to
solve this ill-posed problem, traditional approaches apply depth cues such as defocusing …

In recent years, the development of ground robots with human-like perception capabilities
has led to the use of multiple sensors, including cameras, lidars, and radars, along with …

3D scene reconstruction has important applications to help to produce digital twins of
existing buildings. While the community has mostly focused on surface reconstruction or …

Monocular depth estimation by unsupervised learning is a potential strategy, which is mainly
self-supervised by calculating view reconstruction loss from stereo pairs or monocular …

As cybercriminals scale up their operations to increase their profits or inflict greater harm, we
argue that there is an equal need to respond to their threats by scaling up cyber-security. To …

Robust three-dimensional scene understanding is now an ever-growing area of research
highly relevant in many real-world applications such as autonomous driving and robotic …

Depth map super resolution has been paid much attention in 3D applications due to the
limitation of depth sensors. Few textures in objects with clear contours along them is the …