NL-DSE: Non-Local Neural Network with Decoder-Squeeze-and-Excitation for Monocular Depth Estimation

Monocular Depth Estimation is a popular and challenging problem for many years. IR CNNs (Convolutional Neural Networks)-based method with encoder-decoder architecture is proposed and shows a reasonable result. In this paper, we propose a SE-Net-based module for the decoder part in the encoder-decoder architecture to improve the result. We proposed a DSE (Decoder-Squeeze-and-Excitation) module to deal with the whole up-sampling process globally for the decoder part. We also include the Non-local Network space attention method to design the Non-Local Decoder-Squeeze-and-Excitation (NL-DSE) module. The proposed NL-DSE module is installed and evaluated on the NYU Depth V2 dataset and achieves higher accuracy. Moreover, the design is independent of the encoder-decoder architecture and can be applied in the other encoder-decoder networks to have a more accurate network.