Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Zhelun Shen Xibin Song Yuchao Dai Dingfu Zhou Zhibo Rao Liangjun Zhang
Due to the domain differences and unbalanced disparity distribution across
multiple datasets, current stereo matching approaches are commonly limited to a
specific dataset and generalize poorly to others. Such domain shift issue is
usually addressed by substantial adaptation on costly target-domain
ground-truth data, which cannot be easily obtained in practical settings. In
this paper, we propose to dig into uncertainty estimation for robust stereo
matching. Specifically, to balance the disparity distribution, we employ a
pixel-level uncertainty estimation to adaptively adjust the next stage
disparity searching space, in this way driving the network progressively prune
out the space of unlikely correspondences. Then, to solve the limited ground
truth data, an uncertainty-based pseudo-label is proposed to adapt the
pre-trained model to the new domain, where pixel-level and area-level
uncertainty estimation are proposed to filter out the high-uncertainty pixels
of predicted disparity maps and generate sparse while reliable pseudo-labels to
align the domain gap. Experimentally, our method shows strong cross-domain,
adapt, and joint generalization and obtains \textbf{1st} place on the stereo
task of Robust Vision Challenge 2020. Additionally, our uncertainty-based
pseudo-labels can be extended to train monocular depth estimation networks in
an unsupervised way and even achieves comparable performance with the
supervised methods. The code will be available at
https://github.com/gallenszl/UCFNet.