Local Feature Descriptor Learning with a Dual Hard Sampling Strategy Local feature descriptor learning based on Convolutional Neural Network (CNN) has demonstrated its capability to generate descriptors with high quality. While extensive studies focused on mining hard nonmatching examples to improve descriptor learning performance, a random sampling strategy is adopted for matching examples. In this paper, a dual hard sampling strategy based on the triplet loss function is proposed to generate the hard matching and nonmatching examples for training. To start with, a pair of matching examples with the maximum distance for each class are selected as the positive pair. For each positive pair, their closest nonmatching example is then sampled from the generated positive pairs with other classes as the corresponding negative. Based on the above dual hard sampling strategy, a novel triplet loss function is presented for optimization. With the benefits of the proposed sampling strategy and the novel triplet loss function, our method achieves better performance compared to stateoftheart on the reference benchmark for local feature matching.

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.