@inproceedings{
dai2024eglcr,
title={Eglcr: Edge Structure Guidance and Scale Adaptive Attention for Iterative Stereo Matching},
author={Zhien Dai and Zhaohui Tang and Hu Zhang and Can Tian and Mingjun Pan and Yongfang Xie},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=eJGfwz86Ch}
}
Stereo matching is a pivotal technique for depth estimation and has been popularly applied in various computer vision tasks. Although many related methods have been reported recently, they still face some challenges such as significant disparity variations at object boundaries, difficult prediction at large disparity regions, and suboptimal generalization when label distribution varies between source and target domains. Therefore, we propose a stereo-matching model (i.e., EGLCR-Stereo) that utilizes edge structure information with adaptive fusion of multi-scale matching similarity information for disparity estimation. First, we use a lightweight network to predict the initial disparity. We apply large and small-scale similarity feature extraction modules to extract the matching similarity information within the wide-area receptive field and the refined matching similarity information under the local receptive field. Then, we develop a scale adaptive attention module for efficiently fusing information at different scales. Meanwhile, we propose an edge structure-aware module for exploring edge information in the scene. After that, we use an iterative-based strategy for disparity estimation using edge structure information with fused multi-scale matching similarity information. We conduct abundant experiments on some popular stereo matching datasets including Middlebury, KITTI, ETH3D, and Scene Flow. The results show that our proposed EGLCR-Stereo achieves state-of-the-art performance both in accuracy and generalization.
Eglcr: Edge Structure Guidance and Scale Adaptive Attention for Iterative Stereo Matching
Zhien Dai Zhaohui Tang Hu Zhang Can Tian Mingjun Pan Yongfang Xie