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Bapnp: a barycentric affine invariant linear solver for robust and efficient perspective-n-point pose estimation
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DOI：10.1007/s00371-026-04661-1.png)
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Abstract
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Perspective-n-Point (PnP) pose estimation is a foundational task in computer vision, supporting augmented reality, visual tracking, and structure-from-motion. Existing linear solvers suffer from rank deficiency in quasi-planar scenes, while global optimal methods incur excessive computational cost. This work presents BAPnP, an efficient and robust linear solver built on barycentric affine invariance. A geometry-guided base selection strategy maximizes the reference basis volume to promote a well-conditioned linear system, and an adaptive reduction handles strictly coplanar cases without singularity. Extensive experiments show that BAPnP retains 100% success rate down to strict coplanarity and runs at $$4.4\,\mu s$$ for $$N=10$$ , offering a $$6\times $$ speedup over SQPnP. The method achieves accuracy comparable to global solvers while retaining the efficiency of linear approaches, making it suitable for real-time AR and visual tracking applications. The source code is publicly available at https://github.com/lpl8848/BAPnP_Solver .
Keywords:
Perspective-n-Point
Camera pose estimation
Barycentric affine invariance
Real-time visual tracking
Numerical stability
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