SFVO: Confidence-Guided Stereo-Flow Visual Odometry Turns Disparity and Optical Flow into Bidirectional PnP Constraints
zhenjun_zhao · x · 2026-09-22
A new arXiv paper (2609.21754) by Kai Zhang, Guoyang Zhao, and Jun Ma presents SFVO, a correspondence-driven stereo visual odometry framework.
Key ideas:
- Reuses pretrained stereo matching and optical flow models to estimate dense correspondences, rather than learning pose end-to-end from images;
- Converts disparity and optical flow into forward/backward 3D–2D constraints solved via bidirectional PnP;
- Introduces decoupled confidence maps for rotation and translation, predicting which points are trustworthy and better matching the nature of 6-DoF transforms.
This sidesteps monocular scale ambiguity while avoiding the high computational cost of traditional stereo VO. Experiments on indoor and outdoor datasets show robust, accurate pose estimation with strong generalization. Code will be released.
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