File size: 5,481 Bytes
2b5a2b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | // 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
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
#ifndef COLMAP_SRC_BASE_TRIANGULATION_H_
#define COLMAP_SRC_BASE_TRIANGULATION_H_
#include <vector>
#include <Eigen/Core>
#include "base/camera.h"
#include "util/alignment.h"
#include "util/math.h"
#include "util/types.h"
namespace colmap {
// Triangulate 3D point from corresponding image point observations.
//
// Implementation of the direct linear transform triangulation method in
// R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision,
// Cambridge Univ. Press, 2003.
//
// @param proj_matrix1 Projection matrix of the first image as 3x4 matrix.
// @param proj_matrix2 Projection matrix of the second image as 3x4 matrix.
// @param point1 Corresponding 2D point in first image.
// @param point2 Corresponding 2D point in second image.
//
// @return Triangulated 3D point.
Eigen::Vector3d TriangulatePoint(const Eigen::Matrix3x4d& proj_matrix1,
const Eigen::Matrix3x4d& proj_matrix2,
const Eigen::Vector2d& point1,
const Eigen::Vector2d& point2);
// Triangulate multiple 3D points from multiple image correspondences.
std::vector<Eigen::Vector3d> TriangulatePoints(
const Eigen::Matrix3x4d& proj_matrix1,
const Eigen::Matrix3x4d& proj_matrix2,
const std::vector<Eigen::Vector2d>& points1,
const std::vector<Eigen::Vector2d>& points2);
// Triangulate point from multiple views minimizing the L2 error.
//
// @param proj_matrices Projection matrices of multi-view observations.
// @param points Image observations of multi-view observations.
//
// @return Estimated 3D point.
Eigen::Vector3d TriangulateMultiViewPoint(
const std::vector<Eigen::Matrix3x4d>& proj_matrices,
const std::vector<Eigen::Vector2d>& points);
// Triangulate optimal 3D point from corresponding image point observations by
// finding the optimal image observations.
//
// Note that camera poses should be very good in order for this method to yield
// good results. Otherwise just use `TriangulatePoint`.
//
// Implementation of the method described in
// P. Lindstrom, "Triangulation Made Easy," IEEE Computer Vision and Pattern
// Recognition 2010, pp. 1554-1561, June 2010.
//
// @param proj_matrix1 Projection matrix of the first image as 3x4 matrix.
// @param proj_matrix2 Projection matrix of the second image as 3x4 matrix.
// @param point1 Corresponding 2D point in first image.
// @param point2 Corresponding 2D point in second image.
//
// @return Triangulated optimal 3D point.
Eigen::Vector3d TriangulateOptimalPoint(const Eigen::Matrix3x4d& proj_matrix1,
const Eigen::Matrix3x4d& proj_matrix2,
const Eigen::Vector2d& point1,
const Eigen::Vector2d& point2);
// Triangulate multiple optimal 3D points from multiple image correspondences.
std::vector<Eigen::Vector3d> TriangulateOptimalPoints(
const Eigen::Matrix3x4d& proj_matrix1,
const Eigen::Matrix3x4d& proj_matrix2,
const std::vector<Eigen::Vector2d>& points1,
const std::vector<Eigen::Vector2d>& points2);
// Calculate angle in radians between the two rays of a triangulated point.
double CalculateTriangulationAngle(const Eigen::Vector3d& proj_center1,
const Eigen::Vector3d& proj_center2,
const Eigen::Vector3d& point3D);
std::vector<double> CalculateTriangulationAngles(
const Eigen::Vector3d& proj_center1, const Eigen::Vector3d& proj_center2,
const std::vector<Eigen::Vector3d>& points3D);
} // namespace colmap
#endif // COLMAP_SRC_BASE_TRIANGULATION_H_
|