Underwater stereo matching based on multilevel recurrent field transforms with iterative attentional feature fusion

Jiaqi Leng; Ying Gao; Zhijie Xie; Yanhai Gan; Qingxuan Lv; Hao Fan

doi:10.1117/12.2680190

27 June 2023 Underwater stereo matching based on multilevel recurrent field transforms with iterative attentional feature fusion

Jiaqi Leng, Ying Gao, Zhijie Xie, Yanhai Gan, Qingxuan Lv, Hao Fan

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Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051W (2023) https://doi.org/10.1117/12.2680190
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Stereo matching for depth estimation is a fundamental vision problem. Recent works focus on deep learning to improve accuracy, but most networks encountered the difficulty of poor generalization ability and high computational cost especially on high resolution images. RAFTStereo achieves great advantages in these two aspects, but still can be improved further. In this paper, we revise the residual block of RAFT-Stereo in its feature extractors to improve the performance in underwater scenarios. Specifically, we choose an iterative Attentional Feature Fusion module to utilize the global information in feature fusion. To justify our work, we test our networks on ETH3D benchmark and our own underwater dataset, which demonstrates the superiority of our model as compared to the state-of-the-art baselines. Eventually, comparing to original RAFT-Stereo, our results on ETH3D benchmark outperform by 13.1% on the default metric bad 1-pixel error (percentage of pixels with end-point-errors greater than 1px) and results on our underwater dataset reduce the average error by 16.9%.

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Jiaqi Leng, Ying Gao, Zhijie Xie, Yanhai Gan, Qingxuan Lv, and Hao Fan "Underwater stereo matching based on multilevel recurrent field transforms with iterative attentional feature fusion", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051W (27 June 2023); https://doi.org/10.1117/12.2680190

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KEYWORDS

Feature fusion

Education and training

Feature extraction

Transform theory

3D modeling

Convolution

Liquids

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