关键词: 强化学习, 极线约束, 特征描述子, 神经网络, 损失函数, 图像匹配

Abstract: The learning feature detector applies neural network to detect and match image feature points. Its network parameters are usually trained by optimizing the matching accuracy of the low-level vision. However, in advanced vision tasks, the improvement of low-level matching accuracy may not bring better performance. To address this problem, a double-loss error strategy reinforcement learning method is proposed. On the one hand, the feature points and descriptors obtained by the learning invariant feature transform（LIFT） are represented in probabilistic form. The relative pose between images is estimated, and the pose error is obtained by comparing it with the real pose. On the other hand, the error of descriptor between the matching feature points is estimated using the geometric property of epipolar constraint between matched images. Then, the LIFT is optimized based on the above two loss errors, and finally the neural network parameters are learned. The H-Patches dataset and self-made dataset are used in the experiment, and the images are fed into the LIFT feature detector and vision pipeline, and the neural network parameters are trained in an end-to-end manner. Experimental results show that the proposed algorithm significantly improves the matching accuracy of feature points.

Key words: reinforcement learning, epipolar constraint, feature descriptor, neural network, loss function, image matching