4 citations · 6 across the 4 of their papers we have counts for
4 papers
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task
Weida Yang, Xindong Ai, Zuliu Yang +2
To improve the performance in ill-posed regions, this paper proposes an atrous granular multi-scale network based on depth edge subnetwork(Dedge-AGMNet). According to a general fac…
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching
Li Zhang, Quanhong Wang, Haihua Lu +1
Deep neural networks have shown excellent performance in stereo matching task. Recently CNN-based methods have shown that stereo matching can be formulated as a supervised learning…
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching
Haihua Lu, Hai Xu, Li Zhang +1
Convolutional neural networks(CNN) have been shown to perform better than the conventional stereo algorithms for stereo estimation. Numerous efforts focus on the pixel-wise matchin…
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
Meshia Cédric Oveneke, Mitchel Aliosha-Perez, Yong Zhao +2
The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and s…