Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation
This work addresses a specific bottleneck in encoder-decoder networks for depth estimation, offering incremental improvements in accuracy.
The paper tackles the problem of information loss in skip connections for monocular depth estimation by proposing a full skip connection network (FSCN) and an adaptive concatenation module (ACM), achieving state-of-the-art results on KITTI and NYU Depth V2 datasets.
Skip connections are fundamental units in encoder-decoder networks, which are able to improve the feature propagtion of the neural networks. However, most methods with skip connections just connected features with the same resolution in the encoder and the decoder, which ignored the information loss in the encoder with the layers going deeper. To leverage the information loss of the features in shallower layers of the encoder, we propose a full skip connection network (FSCN) for monocular depth estimation task. In addition, to fuse features within skip connections more closely, we present an adaptive concatenation module (ACM). Further more, we conduct extensive experiments on the ourdoor and indoor datasets (i.e., the KITTI dataste and the NYU Depth V2 dataset) for FSCN and FSCN gets the state-of-the-art results.