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20182021
most citedRadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

11 citations · 19 across the 4 of their papers we have counts for

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8 papers · 1 filter

cs.CV2021

SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving

George Eskandar, Alexander Braun, Martin Meinke +2

Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision…

cs.CV202011 cited

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Bin Yang, Runsheng Guo, Ming Liang +2

We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the…

cs.CV2019

Unsupervised Medical Image Translation Using Cycle-MedGAN

Karim Armanious, Chenming Jiang, Sherif Abdulatif +3

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-re…

cs.CV2019

An Adversarial Super-Resolution Remedy for Radar Design Trade-offs

Karim Armanious, Sherif Abdulatif, Fady Aziz +2

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametr…

cs.CV2018

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Sherif Abdulatif, Fady Aziz, Karim Armanious +3

Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler (-D)…

cs.CV2018

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

Sherif Abdulatif, Karim Armanious, Fady Aziz +2

Generative Adversarial Networks (GANs) are considered the state-of-the-art in the field of image generation. They learn the joint distribution of the training data and attempt to g…