no code implementations • CVPR 2023 • Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, Xing Li
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.
no code implementations • 1 Nov 2020 • Zhibo Rao, Mingyi He, Bo Li, Renjie He
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.
no code implementations • 25 Apr 2019 • Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, Renjie He
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.
no code implementations • 8 Nov 2018 • Mingyang Guan, Zhengguo Li, Renjie He, Changyun Wen
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.