Autonomous Online Self‐Adaptive Stereo Network.
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| Title: | Autonomous Online Self‐Adaptive Stereo Network. |
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| Authors: | Jia, Zihan1 (AUTHOR), Yang, Xiao1 (AUTHOR) 2110293@stu.neu.edu.cn, Zhang, Zheng1 (AUTHOR), Hu, Yige1 (AUTHOR), Jovanovic Dolecek, Gordana1 (AUTHOR) gordana@ieee.org |
| Source: | IET Signal Processing (Wiley-Blackwell). 12/22/2025, Vol. 2025, p1-15. 15p. |
| Subjects: | Stereo vision (Computer science), Stereo image processing, Statistical reliability, Adaptive control systems, Generalization, Trend analysis, Digital technology |
| Abstract: | Existing end‐to‐end stereo matching networks face significant deployment challenges due to their reliance on large training datasets and the limited ability of synthetic data to represent real‐world scenarios, leading to a severe domain shift. To address these challenges, this paper proposes a novel stereo matching network along with an efficient fine‐tuning strategy. First, a lightweight modular stereo matching network is proposed, which incorporates domain knowledge and optimizes specific modules to enhance generalization. Second, a confidence estimation network is developed to generate occlusion masks, which filter erroneous self‐supervised loss. Then, the Mann–Kendall (MK) trend detection method is used to evaluate the loss change trend of the most recent few frames in the online adaptive process to measure the model's adaptation degree to the scene, and control the online operation mode of the model. Finally, online and offline experiments on multiple datasets demonstrate the competitive performance of our method. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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