• DOI:10.48550/arXiv.2209.12699
• Corpus ID: 252531837

Accurate and Efficient Stereo Matching via Attention Concatenation Volume

@article{Xu2022AccurateAE,
  title={Accurate and Efficient Stereo Matching via Attention Concatenation Volume},
  author={Gangwei Xu and Yun Wang and Junda Cheng and Jinhui Tang and Xin Yang},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.12699}
}

• Gangwei Xu, Yun Wang, +2 authors Xin Yang
• Published 23 September 2022
• Computer Science
• ArXiv

—Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. The core idea of our Fast-ACV is volume attention propagation (VAP) which can automatically select accurate correlation values from an upsampled correlation volume and propagate these accurate values to the surroundings pixels with ambiguous correlation clues. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve the state-of-the-art performance on several benchmarks (i.e., our ACVNet ranks the 2 nd on KITTI 2015 and Scene Flow, and the 3 rd on KITTI 2012 and ETH3D among all the published methods; our Fast-ACVNet outperforms almost all state-of-the-art real-time methods on Scene Flow, KITTI 2012 and 2015 and meanwhile has better generalization ability). The source code is available at https://github.com/gangweiX/ACVNet and https://github.com/gangweiX/Fast-ACVNet.