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Fuses stereo and monocular depth cues using a residual correction network to refine sparse SGBM data with monocular predictions.
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The project addresses a specific problem in computer vision—improving depth accuracy by fusing traditional stereo matching (SGBM) with deep monocular priors. While technically sound as a research prototype, it has zero community traction (0 stars/forks) and resides in a space (depth estimation) being rapidly disrupted by zero-shot foundation models like Depth Anything, which diminish the need for complex multi-source fusion for many applications.
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