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The normalized eight-point algorithm has been widely viewed as thecornerstone in two-view geometry computation, where the seminal Hartley'snormalization has greatly improved the performance of the direct lineartransformation algorithm. A natural question is, whether there exists and howto find other normalization methods that may further improve the performance asper each input sample. In this paper, we provide a novel perspective andpropose two contributions to this fundamental problem: 1) we revisit thenormalized eight-point algorithm and make a theoretical contribution bypresenting the existence of different and better normalization algorithms; 2)we introduce a deep convolutional neural network with a self-supervisedlearning strategy for normalization. Given eight pairs of correspondences, ournetwork directly predicts the normalization matrices, thus learning tonormalize each input sample. Our learning-based normalization module can beintegrated with both traditional (e.g., RANSAC) and deep learning frameworks(affording good interpretability) with minimal effort. Extensive experiments onboth synthetic and real images demonstrate the effectiveness of our proposedapproach.

A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution