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 non-matching line. During learning, the goal of the minimization function is to calculate descriptors with minimal descriptor distance to matching lines' descriptors and maximal descriptor distance to other lines' descriptors. We evaluate the performance of our descriptor on our synthetic datasets, on real-world stereo images from the Middlebury Stereo Dataset and on a benchmark for line segment matching. The results show that in comparison to state-of-the-art line descriptors our method achieves a greatly improved line matching accuracy.