We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Besides, we also propose a novel marker correspondence evaluation method circumstancing annotations on real marker-image pairs and create a new benchmark. We show that NeuralMarker significantly outperforms previous methods and enables new interesting applications, including Augmented Reality (AR) and video editing.

翻译：我们处理从一般标记(如电影海报)到捕捉这种标记的图像来估计通信的问题。 公约规定,要解决这个问题的办法是在特征匹配少的基础上设计一个同族体模型。 但是,它们只能处理像飞机一样的标记,而稀少的特征没有充分利用外观信息。 在本文中,我们提出一个新的框架神经标志,培训一个神经网络,在各种具有挑战性的条件下估计密集的标记通信,如标记变形、严酷的照明等。 此外,我们还提议一个新的标记通信评价方法,在实际标记形象配对上进行区分说明,并创建一个新的基准。 我们显示,NeuralMarker明显地超越了以往的方法,并能够进行新的有趣应用,包括放大现实(AR)和视频编辑。