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.
P3P Made Easy
1 Pith paper cite this work. Polarity classification is still indexing.
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 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Non-Minimal Sampling and Consensus for Prohibitively Large Datasets
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.