Depth-Guided Dominant Plane Perception for Unsupervised Homography Estimation

Homography describes the mapping relations of the same plane across views. In scenarios with multiple planes, single homography estimation aims to obtain the optimal solution generated by the largest consistent plane to obey the coplanar constraints. However, existing methods typically consider all planes equally, neglecting the negative impact of regions that differ significantly from the largest approximate planar areas (dominant plane). In this work, we propose a depth-guided dominant plane perception network to achieve unsupervised homography estimation with additional attention on the dominant plane. Specifically, we leverage the depth-wise prior to adaptively detecting the approximate dominant plane, invoking essential scene structures for unsupervised homography estimation. Then, we enhance the corresponding features of the dominant plane and explore their correlations through a specially designed perceptual module. Finally, we employ dominant plane perception on multi-scale features progressively to estimate the homography in a coarse-to-fine manner. Extensive experiments on a large parallax dataset demonstrate that our method improves the alignment performance by 10.29%, yielding more accurate alignment than previous competitive methods.