In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural network-based stereo matching method with multiscale feature connection, named Dense-CNN. First, we construct a novel densely connected network with multiscale convolutional layers to extract rich image features, in which the merged multiscale features with context information are utilized to estimate the cost volume for stereo matching. Second, we plan a novel loss-function strategy to learn the network parameters more reasonably, which can develop the performance of the proposed Dense-CNN model on disparity computation. Finally, we run our Dense-CNN model on the Middlebury and KITTI databases to conduct a comprehensive comparison with several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieved superior performance on computational accuracy and robustness of disparity estimation, especially achieving the significant benefit of feature preservation in ill-posed regions.

尽管基于卷积神经网络的立体匹配模型在准确性和鲁棒性方面均表现出良好的性能，但在缺乏纹理，复杂场景和遮挡的区域中图像特征丢失的问题仍然存在。在本文中，我们提出了一种基于密集卷积神经网络的具有多尺度特征连接的立体匹配方法，称为Dense-CNN。首先，我们构建了一个具有多尺度卷积层的新型密集连接网络，以提取丰富的图像特征，其中利用合并的多尺度特征与上下文信息来估计立体匹配的成本。其次，我们设计了一种新颖的损失函数策略，以更合理地学习网络参数，从而可以提高提出的Dense-CNN模型在视差计算上的性能。最后，我们在Middlebury和KITTI数据库上运行Dense-CNN模型，以与几种最新方法进行全面比较。实验结果表明，该方法在计算精度和视差估计的鲁棒性方面均表现出优异的性能，特别是在不适定区域中保留了特征的显着优势。