Global Occlusion-Aware Transformer for Robust Stereo Matching
Zihua Liu Yizhou Li Masatoshi Okutomi
Despite the remarkable progress facilitated by learning-based stereo-matching
algorithms, the performance in the ill-conditioned regions, such as the
occluded regions, remains a bottleneck. Due to the limited receptive field,
existing CNN-based methods struggle to handle these ill-conditioned regions
effectively. To address this issue, this paper introduces a novel
attention-based stereo-matching network called Global Occlusion-Aware
Transformer (GOAT) to exploit long-range dependency and occlusion-awareness
global context for disparity estimation. In the GOAT architecture, a parallel
disparity and occlusion estimation module PDO is proposed to estimate the
initial disparity map and the occlusion mask using a parallel attention
mechanism. To further enhance the disparity estimates in the occluded regions,
an occlusion-aware global aggregation module (OGA) is proposed. This module
aims to refine the disparity in the occluded regions by leveraging restricted
global correlation within the focus scope of the occluded areas. Extensive
experiments were conducted on several public benchmark datasets including
SceneFlow, KITTI 2015, and Middlebury. The results show that the proposed GOAT
demonstrates outstanding performance among all benchmarks, particularly in the
occluded regions.