Deep Learning Stereo Vision at the edge
This work addresses the problem of enabling computer vision in power-constrained embedded systems, offering an incremental improvement over existing industrial solutions.
The paper tackles stereo vision for embedded devices by developing a hybrid method combining classical techniques and deep learning, achieving the best System on Chip solution on the Middlebury dataset with improved robustness to noise.
We present an overview of the methodology used to build a new stereo vision solution that is suitable for System on Chip. This new solution was developed to bring computer vision capability to embedded devices that live in a power constrained environment. The solution is constructured as a hybrid between classical Stereo Vision techniques and deep learning approaches. The stereoscopic module is composed of two separate modules: one that accelerates the neural network we trained and one that accelerates the front-end part. The system is completely passive and does not require any structured light to obtain very compelling accuracy. With respect to the previous Stereo Vision solutions offered by the industries we offer a major improvement is robustness to noise. This is mainly possible due to the deep learning part of the chosen architecture. We submitted our result to Middlebury dataset challenge. It currently ranks as the best System on Chip solution. The system has been developed for low latency applications which require better than real time performance on high definition videos.