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ceres-solver and colmap
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// Copyright (c) 2022, ETH Zurich and UNC Chapel Hill.
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
// its contributors may be used to endorse or promote products derived
// from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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//
// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
#ifndef COLMAP_SRC_OPTIM_SPRT_H_
#define COLMAP_SRC_OPTIM_SPRT_H_
#include <cmath>
#include <cstddef>
#include <vector>
namespace colmap {
// Sequential Probability Ratio Test as proposed in
//
// "Randomized RANSAC with Sequential Probability Ratio Test",
// Matas et al., 2005
class SPRT {
public:
struct Options {
// Probability of rejecting a good model.
double delta = 0.01;
// A priori assumed minimum inlier ratio
double epsilon = 0.1;
// The ratio of the time it takes to estimate a model from a random sample
// over the time it takes to decide whether one data sample is an
// inlier or not. Matas et al. propose 200 for the 7-point algorithm.
double eval_time_ratio = 200;
// Number of models per random sample, that have to be verified. E.g. 1-3
// for the 7-point fundamental matrix algorithm, or 1-10 for the 5-point
// essential matrix algorithm.
int num_models_per_sample = 1;
};
explicit SPRT(const Options& options);
void Update(const Options& options);
bool Evaluate(const std::vector<double>& residuals, const double max_residual,
size_t* num_inliers, size_t* num_eval_samples);
private:
void UpdateDecisionThreshold();
Options options_;
double delta_epsilon_;
double delta_1_epsilon_1_;
double decision_threshold_;
};
} // namespace colmap
#endif // COLMAP_SRC_OPTIM_SPRT_H_