Abstract
To improve the accuracy of stereo matching, the multi-scale dense attention network (MDA-Net) is proposed. The network introduces two novel modules in the feature extraction stage to achieve better exploit of context information: dual-path upsampling (DU) block and attention-guided context-aware pyramid feature extraction (ACPFE) block. The DU block is introduced to fuse different scale feature maps. It introduces sub-pixel convolution to compensate for the loss of information caused by the traditional interpolation upsampling method. The ACPFE block is proposed to extract multi-scale context information. Pyramid atrous convolution is adopted to exploit multi-scale features and the channel-attention is used to fuse the multi-scale features. The proposed network has been evaluated on several benchmark datasets. The three-pixel-error evaluated over all ground truth pixels is 2.10% on KITTI 2015 dataset. The experiment results prove that MDA-Net achieves state-of-the-art accuracy on KITTI 2012 and 2015 datasets.
Highlights
The depth information of objects is quite important for many computer vision tasks such as three-dimensional reconstruction, robot navigation, and autonomous driving
REVIEW of the Siamese feature extraction module is shown in Figure 2.3 of 12 extraction blocks
The result shows that using dual-path upsampling (DU) blocks to fuse multi-scale features can improve the accuracy of stereo matching effectively
Summary
The depth information of objects is quite important for many computer vision tasks such as three-dimensional reconstruction, robot navigation, and autonomous driving. Obtain context information features for better disparity estimation in the stereo image pairs.trying. A multi-scale dense attention network (MDA-Net) is proposed to exploit to context information for better depth estimation. The block is proposed for high-level features to extract richer context information. The contributions of this contributions of this paper are summarized as follows: paper are summarized as follows: 1. A novel network without any post-processing for stereo matching is proposed; The. DUnetwork block iswithout introduced as a more effective upsampling of fusing multi-scale. A novel any post-processing for stereo matchingmethod is proposed; features; DU block is introduced as a more effective upsampling method of fusing multi-scale features; ACPFE block block is is adopted adopted to to extract extract richer richer context context information.
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