Pyramid Feature Attention Network for Monocular Depth Prediction
Yifang Xu Chenglei Peng Ming Li Yang Li Sidan Du
Deep convolutional neural networks (DCNNs) have achieved great success in
monocular depth estimation (MDE). However, few existing works take the
contributions for MDE of different levels feature maps into account, leading to
inaccurate spatial layout, ambiguous boundaries and discontinuous object
surface in the prediction. To better tackle these problems, we propose a
Pyramid Feature Attention Network (PFANet) to improve the high-level context
features and low-level spatial features. In the proposed PFANet, we design a
Dual-scale Channel Attention Module (DCAM) to employ channel attention in
different scales, which aggregate global context and local information from the
high-level feature maps. To exploit the spatial relationship of visual
features, we design a Spatial Pyramid Attention Module (SPAM) which can guide
the network attention to multi-scale detailed information in the low-level
feature maps. Finally, we introduce scale-invariant gradient loss to increase
the penalty on errors in depth-wise discontinuous regions. Experimental results
show that our method outperforms state-of-the-art methods on the KITTI dataset.