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

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

1 code implementation • 20 Feb 2018 • Oscar Rahnama, Duncan Frost, Ondrej Miksik, Philip H. S. Torr

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.

Stereo Matching Stereo Matching Hand

R$^3$SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems

no code implementations • 30 Oct 2018 • Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Simon Walker, Philip H. S. Torr

Stereo depth estimation is used for many computer vision applications.

Stereo Depth Estimation

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.