2 papers
cs.CV2019
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction
Hamid Hekmatian, Jingfu Jin, Samir Al-Stouhi
This work proposes a method for depth completion of sparse LiDAR data using a convolutional neural network which can be used to generate semi-dense depth maps and "almost" full 3D…
cs.RO2017
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Akansel Cosgun, Lichao Ma, Jimmy Chiu +6
Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has pot…