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SFVO: Decoupled Confidence-Guided Stereo-Flow Visual Odometry with Bidirectional PnP

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from scale ambiguity. Stereo VO provides real metric by its nature, but remains less studied in deep learning VO due to its high computational cost and modeling complexity. Recent advances in stereo matching and optical flow estimation have made dense visual correspondence increasingly accurate and reliable, but their complementary geometric information has not been fully exploited for VO. In this paper, we present SFVO, a correspondence-driven ster

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