RANSAC and robust estimation

Random sample consensus is the usual outer loop around minimal pose and two-view solvers in Theia. pyTheia does not expose the full C++ Estimator / SampleConsensusEstimator template API; you typically call pytheia.sfm helpers that take pytheia.solvers.RansacParameters and pytheia.sfm.RansacType, and return (success, model, pytheia.solvers.RansacSummary). See Geometric estimators.

Variants used by SfM estimators (RansacType)

The factory in create_and_initialize_ransac_variant.h selects the sample-consensus implementation:

pytheia.sfm.RansacType	C++ class	Role
RANSAC	Ransac	Uniform random minimal samples (Fischler). Default for most call sites.
PROSAC	Prosac	Progressive sampling (Chum). Input correspondences must be sorted best-first (e.g. by match score).
LMED	LMed	Least median of squares (Rousseeuw); automatic inlier thresholding, at most ~50% outliers.
EXHAUSTIVE	ExhaustiveRansac	Enumerates minimal samples (only feasible for very small pools; uses ExhaustiveSampler with sample size 2).

Older forks of Theia documented ARRSAC (preemptive / real-time RANSAC) and EVSAC (match-score modeling). Those are not hooked into this factory and are not selectable via RansacType. EVSAC and its vendored stats/optimization dependencies have been removed from this tree; geometric estimators bundled in pyTheia use only the four types above.

RansacParameters (pytheia.solvers)

Aligned with RansacParameters in C++:

Field	Notes
error_thresh	Inlier threshold for the estimator’s residuals (often squared error). You should set this for meaningful results.
failure_probability	Target failure probability (default 0.01).
min_inlier_ratio	Lower bound on inlier fraction used to cap iteration count (default 0).
min_iterations / max_iterations	Iteration bounds.
use_mle	Prefer MLE-style scoring (Torr) instead of raw inlier count.
use_lo / lo_start_iterations	LO-RANSAC-style local refinement when the estimator implements RefineModel.
use_Tdd_test	Reserved for the Td,d test (ChumRandomizedRansac); not implemented in the current loop—leave false.

RansacSummary exposes inliers, num_iterations, confidence, num_lo_iterations, etc., after a run.

SPRT and other internals

Global RANSAC acceleration via sequential probability ratio tests (SPRT) appears in the math layer (Matas); see Math — SPRT. That is separate from the RansacType switch above.

C++: custom estimators

If you extend Theia in C++, the pattern is still: subclass Estimator, then instantiate Ransac<YourEstimator>, Prosac<>, LMed<>, or ExhaustiveRansac<> and call Estimate. New variants plug in by subclassing SampleConsensusEstimator with a custom Sampler / quality metric—then wire them into your own code paths (the core SfM factory only knows the four RansacType values listed here).