Oscar Rahnama — accepted AI-conference papers

2019

Learning to Adapt for Stereo

Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H.S. Torr

CVPR 2019poster

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in the environment, making them less suitable for practical ap…

Cited by 93 PDF ↗code ↗Scholar ↗

2018

Real-Time Dense Stereo Matching With ELAS on FPGA-Accelerated Embedded Devices

Oscar Rahnama, Duncan P. Frost, Ondrej Miksik, Philip H. S. Torr

RA-L 2018

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be

Cited by 33 Source ↗code ↗Scholar ↗