18 citations · 35 across the 7 of their papers we have counts for
7 papers
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching
Zhelun Shen, Zhuo Li, Chenming Wu +4
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their succes…
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Zhelun Shen, Xibin Song, Yuchao Dai +3
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and gene…
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching
Zhelun Shen, Yuchao Dai, Zhibo Rao
Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific…
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
Zhelun Shen, Yuchao Dai, Xibin Song +3
Existing deep learning based stereo matching methods either focus on achieving optimal performances on the target dataset while with poor generalization for other datasets or focus…
MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetry
Yuchao Dai, Zhidong Zhu, Zhibo Rao +1
The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such…
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching
Zhibo Rao, Mingyi He, Yuchao Dai +3
Disparity prediction from stereo images is essential to computer vision applications including autonomous driving, 3D model reconstruction, and object detection. To predict accurat…
Multi-scale Cross-form Pyramid Network for Stereo Matching
Zhidong Zhu, Mingyi He, Yuchao Dai +2
Stereo matching plays an indispensable part in autonomous driving, robotics and 3D scene reconstruction. We propose a novel deep learning architecture, which called CFP-Net, a Cros…