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.

本文提出一种结合SGM和ELAS方法的 FPGA-CPU芯片的立体图像算法，实现了高效和高精度的深度数据点云的计算，并在KITTI 2015数据集上以50FPS的速度和5W的功率消耗下，达到了仅8.7％的误差率。

基于FPGA-CPU混合系统芯片实现功耗较低的实时高精度密集深度