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P3P Made Easy

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

We revisit the classical Perspective-Three-Point (P3P) problem, which aims to recover the absolute pose of a calibrated camera from three 2D-3D correspondences. It has long been known that P3P can be reduced to a quartic polynomial with analytically simple and computationally efficient coefficients. However, this elegant formulation has been largely overlooked in modern literature. Building on the theoretical foundation that traces back to Grunert's work in 1841, we propose a compact algebraic solver that achieves accuracy and runtime comparable to state-of-the-art methods. Our results show that this classical formulation remains highly competitive when implemented with modern insights, offering an excellent balance between simplicity, efficiency, and accuracy.

fields

cs.CV 1

years

2026 1

verdicts

UNVERDICTED 1

representative citing papers

Non-Minimal Sampling and Consensus for Prohibitively Large Datasets

cs.CV · 2026-04-24 · unverdicted · novelty 5.0

NONSAC is a general, estimator-agnostic framework that improves scalability and robustness for geometric model estimation on very large noisy datasets by sampling non-minimal subsets and scoring candidate hypotheses.

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  • Non-Minimal Sampling and Consensus for Prohibitively Large Datasets cs.CV · 2026-04-24 · unverdicted · none · ref 26 · internal anchor

    NONSAC is a general, estimator-agnostic framework that improves scalability and robustness for geometric model estimation on very large noisy datasets by sampling non-minimal subsets and scoring candidate hypotheses.