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Author (up) Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa edit   pdf
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  Title Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
  Year 2020 Publication Sensors Abbreviated Journal SENS  
  Volume 20 Issue 11 Pages 3249  
  Keywords stereo matching; deep learning; embedded GPU  
  Abstract 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.  
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  Area Expedition Conference  
  Notes MSIAU; 600.122 Approved no  
  Call Number Admin @ si @ AAN2020 Serial 3428  
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