CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

By Yuchao Dai, Zhibo Rao. Computer Vision and Pattern Recognition, 2021

390 citations recorded by the source corpus.

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models. In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. First, we propose a fused cost volume representation to deal with the large domain difference. By fusing multiple low-resolution dense cost volumes to enlarge the receptive field, we can extract robust structural representations for initial disparity estimation. Second, we propose a cascade cost volume representation to alleviate the unbalanced disparity distribution. Specifically, we employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space, in this way driving the network progressively prune out the space of unlikely correspondences. By iteratively narrowing down the disparity search space and improving the cost volume resolution, the disparity estimation is gradually refined in a coarse-to-fine manner. When trained on the same training images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed model parameters and hyperparameters, our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust Vision Challenge 2020. The code will be available at https://github.com/gallenszl/CFNet.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/55db23c0f61de1a739f6e8f8708357acfdd6ece2

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Zexiang Xu
2. Kalyan Sunkavalli
3. Sai Bi
4. Junda Cheng
5. Xin Yang
6. Peng Guo
7. Gangwei Xu
8. Kai Zhang

Citations in the corpus

• GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting (2024, methodology): “Recently, deep learning-based MVS methods have also been proposed [16,25,53,69,70], offering efficient high-quality reconstruction in a feed-forward manner.”
• Attention Concatenation Volume for Accurate and Efficient Stereo Matching (2022, methodology): “As shown in Table 3, the EPE is reduced by 39.5% for GwcNet [7], 42.2% for PSMNet [2] and 14.4% for CFNet [16].”