// Copyright (c) 2018, 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.
//
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//       notice, this list of conditions and the following disclaimer in the
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)

#ifndef COLMAP_SRC_ESTIMATORS_EUCLIDEAN_TRANSFORM_H_
#define COLMAP_SRC_ESTIMATORS_EUCLIDEAN_TRANSFORM_H_

#include "base/similarity_transform.h"
#include "util/alignment.h"

namespace colmap {

// N-D Euclidean transform estimator from corresponding point pairs in the
// source and destination coordinate systems.
//
// This algorithm is based on the following paper:
//
//      S. Umeyama. Least-Squares Estimation of Transformation Parameters
//      Between Two Point Patterns. IEEE Transactions on Pattern Analysis and
//      Machine Intelligence, Volume 13 Issue 4, Page 376-380, 1991.
//      http://www.stanford.edu/class/cs273/refs/umeyama.pdf
//
// and uses the Eigen implementation.
template <int kDim>
using EuclideanTransformEstimator = SimilarityTransformEstimator<kDim, false>;

}  // namespace colmap

#endif  // COLMAP_SRC_ESTIMATORS_EUCLIDEAN_TRANSFORM_H_
