Self-supervised monocular depth estimation with multiscale perception
Extracting 3D information from a single optical image is very attractive. Recently emerging
self-supervised methods can learn depth representations without using ground truth depth …
self-supervised methods can learn depth representations without using ground truth depth …
Joint optimization of depth and ego-motion for intelligent autonomous vehicles
The three-dimensional (3D) perception of autonomous vehicles is crucial for localization and
analysis of the driving environment, while it involves massive computing resources for deep …
analysis of the driving environment, while it involves massive computing resources for deep …
3D hierarchical refinement and augmentation for unsupervised learning of depth and pose from monocular video
Depth and ego-motion estimations are essential for the localization and navigation of
autonomous robots and autonomous driving. Recent studies make it possible to learn the …
autonomous robots and autonomous driving. Recent studies make it possible to learn the …
Joint learning of frequency and spatial domains for dense image prediction
Current artificial neural networks mainly conduct the learning process in the spatial domain
but neglect the frequency domain learning. However, the learning course performed in the …
but neglect the frequency domain learning. However, the learning course performed in the …
Self-supervised depth estimation leveraging global perception and geometric smoothness
Self-supervised depth estimation has drawn much attention in recent years as it does not
require labeled data but image sequences. Moreover, it can be conveniently used in various …
require labeled data but image sequences. Moreover, it can be conveniently used in various …
Comprehensive Review of Smart Urban Traffic Management in the Context of the Fourth Industrial Revolution
The Fourth Industrial Revolution (4IR) has ushered in a new era of efficiency across various
domains, including the management of Road Traffic Congestion (RTC) in metropolitan cities …
domains, including the management of Road Traffic Congestion (RTC) in metropolitan cities …
Cbwloss: constrained bidirectional weighted loss for self-supervised learning of depth and pose
F Wang, J Cheng, P Liu - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
Photometric differences are widely used as supervision signals to train neural networks for
estimating depth and camera pose from unlabeled monocular videos. However, this …
estimating depth and camera pose from unlabeled monocular videos. However, this …
DERNet: driver emotion recognition using onboard camera
Driver emotion is considered an essential factor associated with driving behaviors and thus
influences traffic safety. Dynamically and accurately recognizing the emotions of drivers …
influences traffic safety. Dynamically and accurately recognizing the emotions of drivers …
Self-supervised multi-frame monocular depth estimation with pseudo-LiDAR pose enhancement
Depth estimation is one of the most important tasks in scene understanding. In the existing
joint self-supervised learning approaches of depth-pose estimation, depth estimation and …
joint self-supervised learning approaches of depth-pose estimation, depth estimation and …
[HTML][HTML] Self-supervised multi-task learning framework for safety and health-oriented road environment surveillance based on connected vehicle visual perception
Cutting-edge connected vehicle (CV) technologies have drawn much attention in recent
years. The real-time traffic data captured by a CV can be shared with other CVs and data …
years. The real-time traffic data captured by a CV can be shared with other CVs and data …