A multi-focus fusion DOF three-dimensional reconstruction method based on a Siamese network

CN122454053APending Publication Date: 2026-07-24BEIJING BOVISION TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING BOVISION TECH CO LTD
Filing Date
2026-05-09
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing DOF 3D reconstruction methods suffer from unstable focus area judgment under complex imaging conditions, leading to misjudgment of local pixels and affecting the continuity and accuracy of depth reconstruction results.

Method used

The Siamese network is used for multi-focal plane image feature extraction. Focus feature maps are generated through CNN, FPN and DCN. Focus decision maps and confidence maps are generated by combining focal plane depth, threshold and weight parameters. Pixel-level fusion and index matrix update are performed to correct abnormal index jumps and generate optimized 3D reconstruction results.

Benefits of technology

It improves the ability to identify focusing misjudgments in areas with weak texture, high reflectivity, and noise, enhances the continuity and reliability of 3D reconstruction results, and reduces the impact of depth abrupt changes and local errors.

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Abstract

The application discloses a multi-focus fusion DOF three-dimensional reconstruction method based on a Siamese network. The method collects multi-focus plane images of an object to be measured, generates a standardized multi-focus plane image sequence in combination with a calibration parameter set containing a focal plane depth, a threshold parameter group and a weight parameter, inputs adjacent images into a weight sharing Siamese network, extracts a focus feature map in a unified feature space through CNN, FPN and DCN, calculates a focus confidence map according to the focus feature map, and constructs a focus decision map in combination with a focus response difference; pixel-level fusion is completed relying on the decision map and the confidence map, a pixel-level focus index matrix is updated through recursive comparison, index abnormal jumps are corrected by using depth continuity constraints and regional correction weights, an optimized focus index matrix is obtained, depth data and preliminary reconstruction results are generated based on the optimized focus index matrix, abnormal areas are corrected in a reverse tracing manner, and finally, the DOF three-dimensional reconstruction results are output. The method can reduce misjudgment, suppress depth jumps, and improve continuity and accuracy.
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