DLD: A Deep Learning Based Line Descriptor for Line Feature Matching In this paper, we present an appearance based line descriptor which was developed with the help of machine learning. Our descriptor uses a ResNet which we modified in its size to improve the performance. We utilized the Unreal Engine and multiple scenes provided for it to create training data. The training was performed using a triplet loss, where the loss of the network is calculated with triplets each consisting of three lines including a matching pair and another nonmatching line. During learning, the goal of the minimization function is to calculate descriptors with minimal descriptor distance to matching linesxe2x80x99 descriptors and maximal descriptor distance to other linesxe2x80x99 descriptors. We evaluate the performance of our descriptor on our synthetic datasets, on realworld stereo images from the Middlebury Stereo Dataset and on a benchmark for line segment matching. The results show that in comparison to stateoftheart line descriptors our method achieves a greatly improved line matching accuracy.

ImageBased EndtoEnd Neural Network for Dense Disparity Estimation Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel imagebased convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hourglass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the stateoftheart methods in several datasets.