Stereo Matching Algorithm With Deep Learning Method Using Nvidia Platform - Samadi, Masoud; Fauzi Othman, Mohd; Taguchi, Akira (International Journal of Engineering and Technology, 2018)
Autonomous vehicle has become a hot topic for researchers in recent years. One of the important sensors used in these vehicles is Stereo Cameras/Vision. Stereo vision systems are used to estimate the depth from the two cameras installed on the robots or vehicles. This method can deliver the 3D position of all objects captured in the scene at a lower cost and higher density compared to LIDAR. Recently, neural networks are vastly investigated and used in image processing problems and deep learning networks which has surpassed traditional computer vision methods specially in object recognition. In this paper, we propose to use a GPU with a new Siamese deep learning method to speed up the stereo matching algorithm. In this work, we use a high end Nvidia Platform DGX workstation to train and test our algorithm and compare the results with normal GPUs and CPUs. Based on numerical evaluation, the Nvidia DGX can train a neural network with higher input image resolution approximately 8 times faster than a normal GPU and 40 times faster than a Core i7 8 Cores CPU. Since it has the ability to train on a higher resolution the network can be trained in more iteration and results in higher accuracy.Â
Keywords: LIDAR; Stereo Vision; Siamese Deep Neural Network; Nvidia Platform