图像拼接(image stitching)是指将两张或更多的有重叠部分的影像,拼接成一张全景图或是高分辨率影像的技术。图像拼接有两大步骤:图像配准和图像融合

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作者简介: 艾海舟,清华大学计算机系教授,目前主要从事与人脸人体相关的计算机视觉方面的研究,在人脸及人体图像理解领域提出了一系列性能优越的算法,具有重要的学术价值和明显的应用价值。发表论文80余篇,授权国际专利1项,国际专利申请4项。

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Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sight of other available sensors such as inertial measurement unit (IMU) in the above applications. In this paper, we demonstrate that the motion estimation from IMU integration can be used to exploit the spatial distribution prior of keypoints between images. To this end, a probabilistic perspective of attention formulation is proposed to integrate the spatial distribution prior into the attentional graph neural network naturally. With the assistance of spatial distribution prior, the effort of the network for modeling the hidden features can be reduced. Furthermore, we present a projection loss for the proposed keypoints matching network, which gives a smooth edge between matching and un-matching keypoints. Image matching experiments on visual SLAM datasets indicate the effectiveness and efficiency of the presented method.

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