Enhanced local disparity map algorithm segment-side window-based cost aggregation and refinement
Accurate disparity map estimation is crucial for applications such as 3D reconstruction, autonomous navigation, and object detection. Local window-based cost aggregation often suffers from edge fattening and texture inconsistency. This paper introduces a Segment-Side Window based (SSW) stereo matchi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Teknikal Malaysia Melaka
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29399/2/017412512202503832.pdf http://eprints.utem.edu.my/id/eprint/29399/ https://jtec.utem.edu.my/jtec/article/view/6433 |
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| Summary: | Accurate disparity map estimation is crucial for applications such as 3D reconstruction, autonomous navigation, and object detection. Local window-based cost aggregation often suffers from edge fattening and texture inconsistency. This paper introduces a Segment-Side Window based (SSW) stereo matching algorithm that combines Truncated Absolute Difference (TAD), Gradient Magnitude (GM), and Census Transform (CT) to build a robust cost volume. In the proposed approach, SLIC superpixels guide adaptive aggregation, while Side Window Filtering (SWF) preserves edges and enhances texture consistency. Winner-Takes-All optimization and SWF refinement further improve depth accuracy. On the Middlebury dataset, the proposed method achieves 13.3% (Nonocc) and 21.8% (All) bad pixel errors, outperforming BF, GF, iGF, and MF in both edge preservation and texture robustness. |
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