FADNet: A Fast and Accurate Network for Disparity Estimation
- URL: http://arxiv.org/abs/2003.10758v1
- Date: Tue, 24 Mar 2020 10:27:11 GMT
- Title: FADNet: A Fast and Accurate Network for Disparity Estimation
- Authors: Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, Xiaowen Chu
- Abstract summary: We propose an efficient and accurate deep network for disparity estimation named FADNet.
It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation.
It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy.
- Score: 18.05392578461659
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks (DNNs) have achieved great success in the area of
computer vision. The disparity estimation problem tends to be addressed by DNNs
which achieve much better prediction accuracy in stereo matching than
traditional hand-crafted feature based methods. On one hand, however, the
designed DNNs require significant memory and computation resources to
accurately predict the disparity, especially for those 3D convolution based
networks, which makes it difficult for deployment in real-time applications. On
the other hand, existing computation-efficient networks lack expression
capability in large-scale datasets so that they cannot make an accurate
prediction in many scenarios. To this end, we propose an efficient and accurate
deep network for disparity estimation named FADNet with three main features: 1)
It exploits efficient 2D based correlation layers with stacked blocks to
preserve fast computation; 2) It combines the residual structures to make the
deeper model easier to learn; 3) It contains multi-scale predictions so as to
exploit a multi-scale weight scheduling training technique to improve the
accuracy. We conduct experiments to demonstrate the effectiveness of FADNet on
two popular datasets, Scene Flow and KITTI 2015. Experimental results show that
FADNet achieves state-of-the-art prediction accuracy, and runs at a significant
order of magnitude faster speed than existing 3D models. The codes of FADNet
are available at https://github.com/HKBU-HPML/FADNet.
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