  We present a lightweight encoder-decoder architecture for monocular depth
estimation, specifically designed for embedded platforms. Our main contribution
is the Guided Upsampling Block (GUB) for building the decoder of our model.
Motivated by the concept of guided image filtering, GUB relies on the image to
guide the decoder on upsampling the feature representation and the depth map
reconstruction, achieving high resolution results with fine-grained details.
Based on multiple GUBs, our model outperforms the related methods on the NYU
Depth V2 dataset in terms of accuracy while delivering up to 35.1 fps on the
NVIDIA Jetson Nano and up to 144.5 fps on the NVIDIA Xavier NX. Similarly, on
the KITTI dataset, inference is possible with up to 23.7 fps on the Jetson Nano
and 102.9 fps on the Xavier NX. Our code and models are made publicly
available.
