earticle

영어

Global-Local Path Network is a monocular depth estimation network. It presents a new method for integrating global features from an encoder and local features from a decoder through a Selective Feature Fusion module. In this paper, we propose that replacing the SegFormer encoder with the Swin Transformer leads to an improved GLPN, called Swin Transformer-Global-Local-Path-Network. We train the network with modified NYU Depth V2 datasets. Therefore, with the 0.034 RMSE, 0.075 AbsRel, 0.033 log10, 0.951 Delta 1, 0.994 Delta 2, 0.999 Delta 3, our network using a tiny version of Swin Transformer outperforms the previous GLPN model.