A Robust Homography Estimation Method Based on Keypoint Consensus and Appearance Similarity

In this paper, a robust homography estimation method is proposed to match multiview images in the uncalibrated case. This method formulates a new loss function to verify homography hypothesis, which combines models of key-point consensus and appearance similarity. In the consensus model, Lap lace distribution is exploited to better characterize the imprecision of key points. And in the appearance model, a truncated exponential function is utilized to represent the distribution of image similarity values. With these improvements, our method can be more robust in complex situations when a rather high percentage of key points are ambiguous and unreliable, and can output a more accurate homography that satisfies most pixels´ geometric relationship. The experimenttal results highlight the robustness and accuracy of our method in the matching tasks of both synthetic images and real life photos.