  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. Whilst various stereo algorithms have been deployed on
these platforms, usually cut down to better match the embedded architecture,
certain key parts of the more advanced algorithms, e.g. those that rely on
unpredictable access to memory or are highly iterative in nature, are difficult
to deploy efficiently on FPGAs, and thus the depth quality that can be achieved
is limited. In this paper, we leverage a FPGA-CPU chip to propose a novel,
sophisticated, stereo approach that combines the best features of SGM and
ELAS-based methods to compute highly accurate dense depth in real time. Our
approach achieves an 8.7% error rate on the challenging KITTI 2015 dataset at
over 50 FPS, with a power consumption of only 5W.
