PT Journal AU Cristhian A. Aguilera-Carrasco Cristhian Aguilera Cristobal A. Navarro Angel Sappa TI Fast CNN Stereo Depth Estimation through Embedded GPU Devices SO Sensors JI SENS PY 2020 BP 3249 VL 20 IS 11 DI 10.3390/s20113249 DE stereo matching; deep learning; embedded GPU AB Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. ER