Range‐free disparity estimation with self‐adaptive dual‐matching

Abstract

Depth estimation from stereo images is an important task in computer vision. Despite of the great contributions that are made in this field, most matching‐based methods still face the limitations brought by a pre‐set‐fixed disparity range. Stereo matching is reconsidered using a specially designed dual‐matching method with a cross‐attention mechanism, which liberates the algorithm from manually pre‐specified disparity ranges and the performance is guaranteed without re‐training when the camera rig varies. Moreover, to tackle the mismatches on edges and details, an exquisite module is designed based on left‐right consistency, which further refines the estimated disparity map. The efficient multi‐scale aggregation is done with both 2D and 3D convolutional layers and the proposed method is proved to be competitive and effective by experiments conducted under popular benchmarks.