5 papers
Normalized Convolution Upsampling for Refined Optical Flow Estimation
Abdelrahman Eldesokey, Michael Felsberg
Optical flow is a regression task where convolutional neural networks (CNNs) have led to major breakthroughs. However, this comes at major computational demands due to the use of c…
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist +1
The focus in deep learning research has been mostly to push the limits of prediction accuracy. However, this was often achieved at the cost of increased complexity, raising concern…
Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training
Adam Nyberg, Abdelrahman Eldesokey, David Bergström +1
Thermal Infrared (TIR) cameras are gaining popularity in many computer vision applications due to their ability to operate under low-light conditions. Images produced by TIR camera…
Confidence Propagation through CNNs for Guided Sparse Depth Regression
Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data…
Propagating Confidences through CNNs for Sparse Data Regression
Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spac…