Explicit Learning of Feature Orientation Estimation

While many learning-driven algorithms for local feature detection and description have submerged during recent years. One key component in the pipeline, namely orientation estimation, still remains underdeveloped. Among all sorts of difficulties, the impracticality and tedium of finding a "ground truth" feature orientation as a learning target is one big challenge. In this paper, we bypass this "thinking trap" and propose an unsupervised scheme that explicitly trains a simple convolutional neural network to predict orientations for feature points. Together with a carefully designed loss term, the network manages to provide accurate orientation estimations. We further evaluate the capability of this estimator in two experiments: orientation estimation and feature matching. Results showed the proposed method outperforms other compared methods on multiple benchmark datasets. The pretrained model is publicly available.