Leveraging confident points for accurate depth refinement on embedded systems

Despite the notable progress in stereo disparity estimation, algorithms are still prone to errors in challenging conditions. Thus, heuristic disparity refinement techniques are usually deployed to improve accuracy. Moreover, stateof- the-art methods rely on complex CNNs requiring power hungry GPUs not suited for many practical applications constrained by limited computing resources. In this paper, we propose a novel technique for disparity refinement leveraging on confidence measures and a novel, automatic learning-based selection method to discard outliers. Then, a non-local strategy infers missing disparities by analyzing the closest reliable points. This framework is very fast and does not require any hand-tuned thresholding. We assess the performance of our Non-Local Anchoring (NLA), standalone refinement techniques and methods leveraging on confidence measures inside the stereo algorithm. Our evaluation with two popular stereo algorithms shows that our proposal significantly ameliorates their accuracy on Middlebury v3 and KITTI 2015 datasets. Moreover, since our method relies only on cues computed in the disparity domain, it is suited even for COTS stereo cameras coupled with embedded systems, e.g. nVidia Jetson TX2.