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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.
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.
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.
High Speed Tracking With A Fourier Domain Kernelized Correlation Filter
This is achieved due to the attribute of Convolution Theorem that the correlation in spatial domain corresponds to an element-wise product in the Fourier domain, resulting in that the l1-norm optimization problem could be decomposed into multiple sub-optimization spaces in the Fourier domain.
