Leveraging the disparity information from both left and right views is
crucial for stereo disparity estimation. Left-right consistency check is an
effective way to enhance the disparity estimation by referring to the
information from the opposite view. However, the conventional left-right
consistency check is an isolated post-processing step and heavily hand-crafted.
This paper proposes a novel left-right comparative recurrent model to perform
left-right consistency checking jointly with disparity estimation. At each
recurrent step, the model produces disparity results for both views, and then
performs online left-right comparison to identify the mismatched regions which
may probably contain erroneously labeled pixels. A soft attention mechanism is
introduced, which employs the learned error maps for better guiding the model
to selectively focus on refining the unreliable regions at the next recurrent
step. In this way, the generated disparity maps are progressively improved by
the proposed recurrent model. Extensive evaluations on KITTI 2015, Scene Flow
and Middlebury benchmarks validate the effectiveness of our model,
demonstrating that state-of-the-art stereo disparity estimation results can be
achieved by this new model.

本文提出了一种使用递归模型进行立体视觉场景深度估计的方法，并结合左右一致性检查，通过软注意力机制逐步提高生成的视差图质量。在 KITTI 2015，Scene Flow 和 Middlebury 基准测试中进行了广泛的评估，并证明了该方法的有效性和实用价值。

左右相对循环模型用于立体匹配

Left-Right Comparative Recurrent Model for Stereo Matching

We present a method for extracting depth information from a rectified image
pair. We train a convolutional neural network to predict how well two image
patches match and use it to compute the stereo matching cost. The cost is
refined by cross-based cost aggregation and semiglobal matching, followed by a
left-right consistency check to eliminate errors in the occluded regions. Our
stereo method achieves an error rate of 2.61 % on the KITTI stereo dataset and
is currently (August 2014) the top performing method on this dataset.

该论文通过使用卷积神经网络预测图像之间匹配的好坏，进而计算立体匹配代价，再通过交叉基准代价聚合、半全局匹配和左右一致性检查（LRC）来消除遮挡区域中的错误，从而获得了较低的误差率。该方法在 KITTI 立体数据集上达到了 2.61％的误差率，并且是该数据集上目前（2014 年 8 月）表现最好的方法。