Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC

no code implementations • 17 Jul 2019 • Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi Di Stefano, Simon Walker, Philip H. S. Torr

Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs.

Learning to Adapt for Stereo

1 code implementation • CVPR 2019 • Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment.

Autonomous Driving Stereo Depth Estimation

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

no code implementations • 23 May 2018 • Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

The presented algorithms can be applied to any labelling problem using a dense CRF with sparse higher-order potentials.

Semantic Segmentation