Stereo matching is one of the important basic tasks in the computer vision field. In recent years, stereo matching algorithms based on deep learning have achieved excellent performance and become the mainstream research direction. Existing algorithms generally use deep convolutional neural networks (DCNNs) to extract more abstract semantic information, but we believe that the detailed information of the spatial structure is more important for stereo matching tasks. Based on this point of view, this paper proposes a shallow feature extraction network with a large receptive field. The network consists of three parts: a primary feature extraction module, an atrous spatial pyramid pooling (ASPP) module and a feature fusion module. The primary feature extraction network contains only three convolution layers. This network utilizes the basic feature extraction ability of the shallow network to extract and retain the detailed information of the spatial structure. In this paper, the dilated convolution and atrous spatial pyramid pooling (ASPP) module are introduced to increase the size of receptive field. In addition, a feature fusion module is designed, which integrates the feature maps with multiscale receptive fields and mutually complements the feature information of different scales. We replaced the feature extraction part of the existing stereo matching algorithms with our shallow feature extraction network, and achieved state-of-the-art performance on the KITTI 2015 dataset. Compared with the reference network, the number of parameters is reduced by 42%, and the matching accuracy is improved by 1.9%.
The paper at hand argues that shallow feature extraction networks should be favored for the computer vision task of stereo matching, rather than the commonly used deep ResNet backbones. To that end, a model is proposed consisting of three convolutional feature extraction layers only. As this makes it impossible to capture global context, dilated convolutions with varying dilations are applied to the extracted features in parallel and concatenated. Finally, a fusion module implemented via channel attention is applied.
I found this paper lacking in terms of contributions. While the motivation for retaining detailed feature information makes sense for the task in question, it is not clear why it requires replacing the ResNet feature extraction with a very shallow network; an alternative would be to add skip-connections originating from lower levels. Both dilated convolutions and the fusion module are not novel. The paper addresses the need for a fusion module in great detail. However, the insight that a large dilation on top of features computed from a limited receptive field will result in a non-continuous receptive field is a rather trivial one. Hence I don't see the need for section 4.3.1 and 4.3.2, as well as the somewhat complicated deduction in appendix A. The individual modules are ablated, but what about the number of layers for feature extraction? Why settle for 3 rather than 1, 2, or 4?
The paper does compare against other methods for stereo matching, but I was wondering why the KTTI leader-board excerpt does not include the better results from http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo ? The result tables are also hard to parse as bold numbers do not correspond to best performance. The conclusion claims that the proposed shallow architecture exhibits "lower training difficulty" but I did not see any support for this in the experiments.
Overall, I think that this paper should be rejected on the basis of insufficient contributions.