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Robust homography estimation between two images is a fundamental task which has been widely applied to various vision applications. Traditional feature based methods often detect image features and fit a homography according to matched features with RANSAC outlier removal. However, the quality of homography heavily relies on the quality of image features, which are prone to errors with respect to low light and low texture images. On the other hand, previous deep homography approaches either synthesize images for supervised learning or adopt aerial images for unsupervised learning, both ignoring the importance of depth disparities in homography estimation. Moreover, they treat the image content equally, including regions of dynamic objects and near-range foregrounds, which further decreases the quality of estimation. In this work, to overcome such problems, we propose an unsupervised deep homography method with a new architecture design. We learn a mask during the estimation to reject outlier regions. In addition, we calculate loss with respect to our learned deep features instead of directly comparing the image contents as did previously. Moreover, a comprehensive dataset is presented, covering both regular and challenging cases, such as poor textures and non-planar interferences. The effectiveness of our method is validated through comparisons with both feature-based and previous deep-based methods. Code will be soon available at Github 1 .

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Data association is important in the point cloud registration. In this work, we propose to solve the partial-to-partial registration from a new perspective, by introducing multi-level feature interactions between the source and the reference clouds at the feature extraction stage, such that the registration can be realized without the attentions or explicit mask estimation for the overlapping detection as adopted previously. Specifically, we present FINet, a feature interactionbased structure with the capability to enable and strengthen the information associating between the inputs at multiple stages. To achieve this, we first split the features into two components, one for rotation and one for translation, based on the fact that they belong to different solution spaces, yielding a dual branches structure. Second, we insert several interaction modules at the feature extractor for the data association. Third, we propose a transformation sensitivity loss to obtain rotation-attentive and translation-attentive features. Experiments demonstrate that our method performs higher precision and robustness compared to the state-of-the-art traditional and learning-based methods. Code is available at https://github.com/megvii-research/FINet.

Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane-induced parallax, which will make the predicted homography compromised on multiple planes. In this work, we propose a novel method HomoGAN to guide unsupervised homography estimation to focus on the dominant plane. First, a multi-scale transformer network is designed to predict homography from the feature pyramids of input images in a coarse-to-fine fashion. Moreover, we propose an unsupervised GAN to impose coplanarity constraint on the predicted homography, which is realized by using a generator to predict a mask of aligned regions, and then a discriminator to check if two masked feature maps are induced by a single homography. To validate the effectiveness of HomoGAN and its components, we conduct extensive experiments on a large-scale dataset, and results show that our matching error is 22% lower than the previous SOTA method. Code is available at https: //github.com/megvii-research/HomoGAN .