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Found 9 papers, 3 papers with code
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others.
Masked Representation Learning for Domain Generalized Stereo Matching
Experimental results on multi-datasets show that: (1) our method can be easily plugged into the current various stereo matching models to improve generalization performance; (2) our method can reduce the significant volatility of generalization performance among different training epochs; (3) we find that the current methods prefer to choose the best results among different training epochs as generalization performance, but it is impossible to select the best performance by ground truth in practice.
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching
In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network.
Class Attention Network for Semantic Segmentation of Remote Sensing Images
In this paper, we proposed a novel class attention module and decomposition-fusion strategy to cope with imbalanced labels.
NLCA-Net v2 for Stereo Matching in ECCV'20 Robust Vision Challenge
The network architecture used in this RVC, called as NLCA-Net v2, is consists of four parts: feature extraction, cost volume construction, feature matching, and refinement, as shown in Fig.
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetry
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.
Multi-scale Cross-form Pyramid Network for Stereo Matching
The network consists of three modules: Multi-Scale 2D local feature extraction module, Cross-form spatial pyramid module and Multi-Scale 3D Feature Matching and Fusion module.
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching
The multi-scale residual 3D convolution module learns the different scale geometry context from the cost volume which aggregated by the multi-scale fusion 2D convolution module.
