Monocular depth estimation is a basic task in machine vision. In recent years, the performance of monocular depth estimation has been greatly improved. However, most depth estimation networks are based on a very deep network to extract features that lead to a large amount of information lost. The loss of object information is particularly serious in the encoding and decoding process. This information loss leads to the estimated depth maps lacking object structure detail and have non-clear edges. Especially in a complex indoor environment, which is our research focus in this paper, the consequences of this loss of information are particularly serious. To solve this problem, we propose a Dense feature fusion network that uses a feature pyramid to aggregate various scale features. Furthermore, to improve the fusion effectiveness of decoded object contour information and depth information, we propose an adaptive depth fusion module, which allows the fusion network to fuse various scale depth maps adaptively to increase object information in the predicted depth map. Unlike other work predicting depth maps relying on U-NET architecture, our depth map predicted by fusing multi-scale depth maps. These depth maps have their own characteristics. By fusing them, we can estimate depth maps that not only include accurate depth information but also have rich object contour and structure detail. Experiments indicate that the proposed model can predict depth maps with more object information than other prework, and our model also shows competitive accuracy. Furthermore, compared with other contemporary techniques, our method gets state-of-the-art in edge accuracy on the NYU Depth V2 dataset.
Stereo matching estimates the disparity between a pair of rectified left and right images, which plays an important role in robot navigation, autonomous driving, and other related tasks. Despite the remarkable progress of deep learning in stereo matching, there are still many challenges. One of the challenges is that current stereo models mostly generate a single scale cost volume based on costly 3D convolutions method or quick 2D convolutions method, but neither of these two methods can achieve a fair trade-off between quality and time. In this paper, we propose to construct multi-scale hybrid cost volume which aims at achieving fast speed while maintaining comparable accuracy. Concretely, we generate the correlation cost volume and the concatenation cost volume respectively, and then integrate together to form a hybrid cost volume which can significantly improve the accuracy and reduce the computational complexity. At the multi-scale level, we generate three hybrid cost volumes at different scales and then aggregate them by 2D convolutions which are faster than 3D convolutions. In addition, we adopt 2D CNN stacked hourglass with fused cost volume for cost aggregation. Specifically, the proposed method provides competitive performance with state-of-the-art methods, while being faster than most top-performing methods (e.g., 3.7× than PSMNet, 8.1× than GCNet, 16.3× than GANet). Extensive experiments of our model on the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the competitiveness of our results.INDEX TERMS Stereo matching, disparity estimation, convolutional neural network, cost volume.
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