Robust stereo matching method based on underwater prior driving data and feature enhancement

By employing physical-guided geometric synthesis and significant structure-guided enhancement methods, a robust stereo matching method is generated, which solves the problems of appearance degradation and geometric distortion in underwater environments. This method achieves high-precision and flexible underwater stereo matching, suitable for marine exploration and robot navigation.

CN122156944APending Publication Date: 2026-06-05SHANDONG UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG UNIV OF SCI & TECH
Filing Date
2026-02-03
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing stereo matching methods suffer from low matching accuracy and poor generalization ability in underwater environments due to appearance degradation and geometric distortion, and also lack a large amount of real underwater stereo annotation data.

Method used

A robust stereo matching method based on underwater prior-driven data and feature enhancement is adopted. It generates domain-aware stereo image pairs through physical-guided geometric synthesis, enhances feature extraction with salient structure guidance, and iteratively refines the disparity map at high resolution to achieve rapid adaptation of the target domain.

Benefits of technology

It improves the accuracy and generalization ability of underwater 3D matching, enabling it to flexibly cope with different water turbidity and lighting conditions in the absence of labeled data, significantly reducing the mismatch rate and improving the application efficiency of marine robot platforms.

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Abstract

The application discloses a robust stereo matching method based on underwater prior driving data and feature enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: a physical guidance geometry synthesis module is used to simulate nonlinear geometric distortion caused by underwater refraction and the like by using monocular images and depth prior explicit simulation, and physical consistent stereo training pairs are generated; a salient structure guidance enhancement module is used to extract and fuse scene salient structure representation through an attention mechanism, and the discriminability of features in weak texture areas is improved; and a coarse-to-fine disparity prediction and adaptive process is used. The application is mainly used in underwater robot navigation, ocean resource exploration and underwater structure detection and the like, can effectively overcome the matching problem caused by appearance degradation and geometric distortion of underwater images, realizes high-precision and strong-generalization depth estimation without real stereo labeled data, supports fast adaptation only by using monocular images of a target scene, and improves the practicability and flexibility of the method.
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