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
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
|
// Copyright (c) 2021, Viktor Larsson
// 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 the copyright holder 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 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.
#ifndef POSELIB_ROBUST_ESTIMATORS_ABSOLUTE_POSE_H
#define POSELIB_ROBUST_ESTIMATORS_ABSOLUTE_POSE_H
#include "PoseLib/camera_pose.h"
#include "PoseLib/robust/sampling.h"
#include "PoseLib/robust/utils.h"
#include "PoseLib/types.h"
namespace poselib {
class AbsolutePoseEstimator {
public:
AbsolutePoseEstimator(const RansacOptions &ransac_opt, const std::vector<Point2D> &points2D,
const std::vector<Point3D> &points3D)
: num_data(points2D.size()), opt(ransac_opt), x(points2D), X(points3D),
sampler(num_data, sample_sz, opt.seed, opt.progressive_sampling, opt.max_prosac_iterations) {
xs.resize(sample_sz);
Xs.resize(sample_sz);
sample.resize(sample_sz);
}
void generate_models(std::vector<CameraPose> *models);
double score_model(const CameraPose &pose, size_t *inlier_count) const;
void refine_model(CameraPose *pose) const;
const size_t sample_sz = 3;
const size_t num_data;
private:
const RansacOptions &opt;
const std::vector<Point2D> &x;
const std::vector<Point3D> &X;
RandomSampler sampler;
// pre-allocated vectors for sampling
std::vector<Point3D> xs, Xs;
std::vector<size_t> sample;
};
class GeneralizedAbsolutePoseEstimator {
public:
GeneralizedAbsolutePoseEstimator(const RansacOptions &ransac_opt, const std::vector<std::vector<Point2D>> &points2D,
const std::vector<std::vector<Point3D>> &points3D,
const std::vector<CameraPose> &camera_ext)
: num_cams(points2D.size()), opt(ransac_opt), x(points2D), X(points3D), rig_poses(camera_ext) {
rng = opt.seed;
ps.resize(sample_sz);
xs.resize(sample_sz);
Xs.resize(sample_sz);
sample.resize(sample_sz);
camera_centers.resize(num_cams);
for (size_t k = 0; k < num_cams; ++k) {
camera_centers[k] = camera_ext[k].center();
}
num_data = 0;
num_pts_camera.resize(num_cams);
for (size_t k = 0; k < num_cams; ++k) {
num_pts_camera[k] = points2D[k].size();
num_data += num_pts_camera[k];
}
}
void generate_models(std::vector<CameraPose> *models);
double score_model(const CameraPose &pose, size_t *inlier_count) const;
void refine_model(CameraPose *pose) const;
const size_t sample_sz = 3;
size_t num_data;
const size_t num_cams;
private:
const RansacOptions &opt;
const std::vector<std::vector<Point2D>> &x;
const std::vector<std::vector<Point3D>> &X;
const std::vector<CameraPose> &rig_poses;
std::vector<Point3D> camera_centers;
std::vector<size_t> num_pts_camera; // number of points in each camera
RNG_t rng;
// pre-allocated vectors for sampling
std::vector<Point3D> ps, xs, Xs;
std::vector<std::pair<size_t, size_t>> sample;
};
class AbsolutePosePointLineEstimator {
public:
AbsolutePosePointLineEstimator(const RansacOptions &ransac_opt, const std::vector<Point2D> &x,
const std::vector<Point3D> &X, const std::vector<Line2D> &l,
const std::vector<Line3D> &L)
: num_data(x.size() + l.size()), opt(ransac_opt), points2D(x), points3D(X), lines2D(l), lines3D(L) {
rng = opt.seed;
xs.resize(sample_sz);
Xs.resize(sample_sz);
ls.resize(sample_sz);
Cs.resize(sample_sz);
Vs.resize(sample_sz);
sample.resize(sample_sz);
}
void generate_models(std::vector<CameraPose> *models);
double score_model(const CameraPose &pose, size_t *inlier_count) const;
void refine_model(CameraPose *pose) const;
const size_t sample_sz = 3;
const size_t num_data;
private:
const RansacOptions &opt;
const std::vector<Point2D> &points2D;
const std::vector<Point3D> &points3D;
const std::vector<Line2D> &lines2D;
const std::vector<Line3D> &lines3D;
RNG_t rng;
// pre-allocated vectors for sampling
std::vector<Point3D> xs, Xs, ls, Cs, Vs;
std::vector<size_t> sample;
};
class Radial1DAbsolutePoseEstimator {
public:
Radial1DAbsolutePoseEstimator(const RansacOptions &ransac_opt, const std::vector<Point2D> &points2D,
const std::vector<Point3D> &points3D)
: num_data(points2D.size()), opt(ransac_opt), x(points2D), X(points3D),
sampler(num_data, sample_sz, opt.seed, opt.progressive_sampling, opt.max_prosac_iterations) {
xs.resize(sample_sz);
Xs.resize(sample_sz);
sample.resize(sample_sz);
}
void generate_models(std::vector<CameraPose> *models);
double score_model(const CameraPose &pose, size_t *inlier_count) const;
void refine_model(CameraPose *pose) const;
const size_t sample_sz = 5;
const size_t num_data;
private:
const RansacOptions &opt;
const std::vector<Point2D> &x;
const std::vector<Point3D> &X;
RandomSampler sampler;
// pre-allocated vectors for sampling
std::vector<Point2D> xs;
std::vector<Point3D> Xs;
std::vector<size_t> sample;
};
} // namespace poselib
#endif
|
