Depth Estimation with MultiResolution Stereo Matching Depth estimation has widely demands in autopilot and scene reconstruction. Although depth estimation has been greatly improved by deep learning, there is still some room for improvement. Usually, stereomatchingbased depth estimation matches lowresolution features and then upsamples depth map to fullresolution. Such methods suffer from low accuracy because of information loss in lowresolution features. To solve this problem, a depth estimation method via multiresolution gradualrefining stereo matching is proposed. As with classic methods, this method first extracts pyramid features by convolution network and estimates lowresolution depth maps. Then, as an innovation, stereomatching at each pyramid feature is successively executed using the lowresolution maps as initial depth, which limits search range of stereomatching for accuracy and efficiency. Results of stereomatching are used as residuals to refine the depth maps. Experimental results demonstrate that accuracy of depth estimation by the proposed method is significantly improved, but computational complexity does not much increase compared with classic methods.

Binocular Depth Estimation Using Convolutional Neural Network With Siamese Branches Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two subnetworks. The first subnetwork is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two subnetworks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods.