This research presents a novel depth estimation algorithm based on a Transformer-encoder
architecture, tailored for the NYU and KITTI Depth Dataset. This research adopts a …

Effective 3D perception of an observed scene greatly enriches the knowledge about the
surrounding environment and is crucial to effectively develop high-level applications for …

3D scene understanding is crucial for robotics, augmented reality and autonomous vehicles.
In those applications, the 3D structure can be computed by using stereo cameras or depth …

Distance estimation is required for advanced driver as-sistance systems (ADAS) as well as
self-driving cars. It is crucial for obstacle avoidance, tailgating detection and accident …

In numerous applications, such as collision detection, Robotic handling, Robotic-based
manufacturing facility, and Advanced Driver Assistance Systems (ADAS), depth estimation is …

Among the various cues that help us understand and interact with our surroundings, depth is
of particular importance. It allows us to move in space and grab objects to complete different …

Depth estimation approaches are crucial for environment perception in applications like
autonomous driving or driving assistance systems. Solutions using cameras have always …

Monocular depth estimation is a popular research topic in the field of autonomous driving.
Nowadays many models are leading in accuracy but performing poorly in a real-time …

Depth estimation can provide tremendous help for object detection, localization, path
planning, etc. However, the existing methods based on deep learning have high …

Depth perception has become a heavily researched area as companies and researchers
are striving towards the development of self-driving cars. Self-driving cars rely on perceiving …