Volumetric deep learning approach towards stereo matching aggregates a cost
volume computed from input left and right images using 3D convolutions. Recent
works showed that utilization of extracted image features and a spatially
varying cost volume aggregation complements 3D convolutions. However, existing
methods with spatially varying operations are complex, cost considerable
computation time, and cause memory consumption to increase. In this work, we
construct Guided Cost volume Excitation (GCE) and show that simple channel
excitation of cost volume guided by image can improve performance considerably.
Moreover, we propose a novel method of using top-k selection prior to
soft-argmin disparity regression for computing the final disparity estimate.
Combining our novel contributions, we present an end-to-end network that we
call Correlate-and-Excite (CoEx). Extensive experiments of our model on the
SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the effectiveness
and efficiency of our model and show that our model outperforms other
speed-based algorithms while also being competitive to other state-of-the-art
algorithms. Codes will be made available at this https URL

通过引导代价体增加通道激励以及使用前 k 项选择，本文构建的 CoEx 模型将 3D 卷积与空间变化耦合起来，提高了立体匹配的性能。在 SceneFlow、KITTI 2012 和 KITTI 2015 数据集上的实验结果证明了本模型的有效性和高效性，优于其他速度算法且与其他现有算法具有可比性。

Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume  Excitation

We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches.

该文提出了一种基于深度学习的新型架构，用于处理立体图像的视差回归问题，通过代价体积和不同 iable sof argmin 算法来进行图像处理，并在 KITTI 数据集上创造了新的最佳性能结果。