  Pre-training has emerged as a simple yet powerful methodology for
representation learning across various domains. However, due to the expensive
training cost and limited data, pre-training has not yet been extensively
studied in correspondence pruning. To tackle these challenges, we propose a
pre-training method to acquire a generic inliers-consistent representation by
reconstructing masked correspondences, providing a strong initial
representation for downstream tasks. Toward this objective, a modicum of true
correspondences naturally serve as input, thus significantly reducing
pre-training overhead. In practice, we introduce CorrMAE, an extension of the
mask autoencoder framework tailored for the pre-training of correspondence
pruning. CorrMAE involves two main phases, \ie correspondence learning and
matching point reconstruction, guiding the reconstruction of masked
correspondences through learning visible correspondence consistency. Herein, we
employ a dual-branch structure with an ingenious positional encoding to
reconstruct unordered and irregular correspondences. Also, a bi-level designed
encoder is proposed for correspondence learning, which offers enhanced
consistency learning capability and transferability. Extensive experiments have
shown that the model pre-trained with our CorrMAE outperforms prior work on
multiple challenging benchmarks. Meanwhile, our CorrMAE is primarily a
task-driven pre-training method, and can achieve notable improvements for
downstream tasks by pre-training on the targeted dataset. We hope this work can
provide a starting point for correspondence pruning pre-training.
