Depth estimation from stereo or multi-view images is of substantial interest due to a wide range of applications. Recently, deep learning based approaches have been shown to be promising for stereo matching. However, existing stereo matching approaches are mostly data-driven, which often converge to local minima biased toward the training data. In this paper, we propose a simple but effective regularization framework to improve the training of the stereo matching networks. More specifically, we propose using low-level structure detection such as edge detection and keypoint detection as constraints for the regularization of the stereo matching network via multi-task learning. By introducing the low-level structure detection as an auxiliary task, we are able to improve the model training of stereo matching. In addition, a disparity aggregation module is also proposed to consider the association between the stereo matching and low-level structures. We apply the proposed structure regularization on four different CNN-based stereo matching algorithms. The experimental results on four public datasets, including Scene Flow, KITTI 2012, KITTI 2015 and Middlebury, verify our assumptions and show the effectiveness and generality of the proposed framework.
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Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021