Uncertainty-Adaptive Volume for Unsupervised Homography Estimation

Estimating homography from an image pair is crucial for image alignment, and unsupervised methods that optimize feature reprojection error between target and warped source images have gained attention for their promising performance. In real-world scenes with multiple planes, such as moving objects, outlier rejection strategies are essential to mitigate the influence of non-dominant planes. Existing methods address this by learning a mask based on reprojection error, where high errors indicate non-dominant planes misaligned by homography. However, this error-fitting mask often overextends to the dominant plane, limiting the use of valid image regions for accurate estimation. This paper proposes a novel unsupervised method to compactly exclude non-dominant planes by introducing an uncertainty-adaptive cost volume for homography estimation. We first model uncertainty by assuming image features follow a Gaussian distribution derived from a prior Normal Inverse-Gamma distribution. The network-learned distribution parameters disentangle aleatoric uncertainty, distinguishing data-dependent errors within the total reprojection error. This uncertainty reflects inherent observation noise in image data, effectively indicating non-dominant planes. We then integrate this aleatoric uncertainty into the concatenation volume across image feature maps, creating an adaptive volume that filters out unreliable matching costs associated with non-dominant planes. This adaptive volume simplifies learning homography from the rich, redundant content in the concatenation volume, enabling more efficient and accurate estimation. Experiments demonstrate that our method outperforms existing approaches, achieving state-of-the-art performance both qualitatively and quantitatively.