1 文章说明
方向：立体匹配
会议：CVPR2019
2 动机
However, existing methods only use features from plain convolution layers or a simple aggregation of multi-level features to calculate cost volume, which is insufficient because stereo matching requires discriminative features to identify corresponding pixels in rectified stereo image pairs. In this paper, we propose a unary features descriptor using multi-level context ultra-aggregation (MCUA), which encapsulates all convolutional features into a more discriminative representation by intra- and inter-level features combination.
提出了一个更好的结构来提取更好的特征
3 核心
Multi-level Context Ultra-Aggregation (MCUA) scheme which combines the features at the shallowest, smallest scale and deeper, larger scales using just “shallow” skip connections.
提出的框架：
提出的核心模块：
1 通过上面的分支提取全局特征
2 通过下面的分支提取局部特征
3 通过短连接来融合不同尺度的局部和全局特征
4 数据库
The Scene Flow datasets ： 训练 35454
测试：4370
像素：1242
KITTI2015/2012 ： 训练 200/194
测试：200/195
像素：1242/375
5训练
优化器：Adam (Adaptive Moment Estimation)
batch: 8
maximum disparity (D):192 pixels
Scene Flow datasets: 70 epochs, 256 × 512 resolution
KITTI2015/2012 :
6 实验
