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.