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coordinate_frame.cc
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coordinate_frame.cc
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// 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.
//
// * 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)
#include "estimators/coordinate_frame.h"
#include "base/gps.h"
#include "base/line.h"
#include "base/pose.h"
#include "base/undistortion.h"
#include "estimators/utils.h"
#include "optim/ransac.h"
#include "util/logging.h"
#include "util/misc.h"
namespace colmap {
namespace {
struct VanishingPointEstimator {
// The line segments.
typedef LineSegment X_t;
// The line representation of the segments.
typedef Eigen::Vector3d Y_t;
// The vanishing point.
typedef Eigen::Vector3d M_t;
// The minimum number of samples needed to estimate a model.
static const int kMinNumSamples = 2;
// Estimate the vanishing point from at least two line segments.
static std::vector<M_t> Estimate(const std::vector<X_t>& line_segments,
const std::vector<Y_t>& lines) {
CHECK_EQ(line_segments.size(), 2);
CHECK_EQ(lines.size(), 2);
return {lines[0].cross(lines[1])};
}
// Calculate the squared distance of each line segment's end point to the line
// connecting the vanishing point and the midpoint of the line segment.
static void Residuals(const std::vector<X_t>& line_segments,
const std::vector<Y_t>& lines,
const M_t& vanishing_point,
std::vector<double>* residuals) {
residuals->resize(line_segments.size());
// Check if vanishing point is at infinity.
if (vanishing_point[2] == 0) {
std::fill(residuals->begin(), residuals->end(),
std::numeric_limits<double>::max());
return;
}
for (size_t i = 0; i < lines.size(); ++i) {
const Eigen::Vector3d midpoint =
(0.5 * (line_segments[i].start + line_segments[i].end)).homogeneous();
const Eigen::Vector3d connecting_line = midpoint.cross(vanishing_point);
const double signed_distance =
connecting_line.dot(line_segments[i].end.homogeneous()) /
connecting_line.head<2>().norm();
(*residuals)[i] = signed_distance * signed_distance;
}
}
};
Eigen::Vector3d FindBestConsensusAxis(const std::vector<Eigen::Vector3d>& axes,
const double max_distance) {
if (axes.empty()) {
return Eigen::Vector3d::Zero();
}
std::vector<int> inlier_idxs;
inlier_idxs.reserve(axes.size());
std::vector<int> best_inlier_idxs;
best_inlier_idxs.reserve(axes.size());
double best_inlier_distance_sum = std::numeric_limits<double>::max();
for (size_t i = 0; i < axes.size(); ++i) {
const Eigen::Vector3d ref_axis = axes[i];
double inlier_distance_sum = 0;
inlier_idxs.clear();
for (size_t j = 0; j < axes.size(); ++j) {
if (i == j) {
inlier_idxs.push_back(j);
} else {
const double distance = 1 - ref_axis.dot(axes[j]);
if (distance <= max_distance) {
inlier_distance_sum += distance;
inlier_idxs.push_back(j);
}
}
}
if (inlier_idxs.size() > best_inlier_idxs.size() ||
(inlier_idxs.size() == best_inlier_idxs.size() &&
inlier_distance_sum < best_inlier_distance_sum)) {
best_inlier_distance_sum = inlier_distance_sum;
best_inlier_idxs = inlier_idxs;
}
}
if (best_inlier_idxs.empty()) {
return Eigen::Vector3d::Zero();
}
Eigen::Vector3d best_axis(0, 0, 0);
for (const auto idx : best_inlier_idxs) {
best_axis += axes[idx];
}
best_axis /= best_inlier_idxs.size();
return best_axis;
}
} // namespace
Eigen::Vector3d EstimateGravityVectorFromImageOrientation(
const Reconstruction& reconstruction, const double max_axis_distance) {
std::vector<Eigen::Vector3d> downward_axes;
downward_axes.reserve(reconstruction.NumRegImages());
for (const auto image_id : reconstruction.RegImageIds()) {
const auto& image = reconstruction.Image(image_id);
downward_axes.push_back(image.RotationMatrix().row(1));
}
return FindBestConsensusAxis(downward_axes, max_axis_distance);
}
Eigen::Matrix3d EstimateManhattanWorldFrame(
const ManhattanWorldFrameEstimationOptions& options,
const Reconstruction& reconstruction, const std::string& image_path) {
std::vector<Eigen::Vector3d> rightward_axes;
std::vector<Eigen::Vector3d> downward_axes;
for (size_t i = 0; i < reconstruction.NumRegImages(); ++i) {
const auto image_id = reconstruction.RegImageIds()[i];
const auto& image = reconstruction.Image(image_id);
const auto& camera = reconstruction.Camera(image.CameraId());
PrintHeading1(StringPrintf("Processing image %s (%d / %d)",
image.Name().c_str(), i + 1,
reconstruction.NumRegImages()));
std::cout << "Reading image..." << std::endl;
colmap::Bitmap bitmap;
CHECK(bitmap.Read(colmap::JoinPaths(image_path, image.Name())));
std::cout << "Undistorting image..." << std::endl;
UndistortCameraOptions undistortion_options;
undistortion_options.max_image_size = options.max_image_size;
Bitmap undistorted_bitmap;
Camera undistorted_camera;
UndistortImage(undistortion_options, bitmap, camera, &undistorted_bitmap,
&undistorted_camera);
std::cout << "Detecting lines...";
const std::vector<LineSegment> line_segments =
DetectLineSegments(undistorted_bitmap, options.min_line_length);
const std::vector<LineSegmentOrientation> line_orientations =
ClassifyLineSegmentOrientations(line_segments,
options.line_orientation_tolerance);
std::cout << StringPrintf(" %d", line_segments.size());
std::vector<LineSegment> horizontal_line_segments;
std::vector<LineSegment> vertical_line_segments;
std::vector<Eigen::Vector3d> horizontal_lines;
std::vector<Eigen::Vector3d> vertical_lines;
for (size_t i = 0; i < line_segments.size(); ++i) {
const auto line_segment = line_segments[i];
const Eigen::Vector3d line_segment_start =
line_segment.start.homogeneous();
const Eigen::Vector3d line_segment_end = line_segment.end.homogeneous();
const Eigen::Vector3d line = line_segment_start.cross(line_segment_end);
if (line_orientations[i] == LineSegmentOrientation::HORIZONTAL) {
horizontal_line_segments.push_back(line_segment);
horizontal_lines.push_back(line);
} else if (line_orientations[i] == LineSegmentOrientation::VERTICAL) {
vertical_line_segments.push_back(line_segment);
vertical_lines.push_back(line);
}
}
std::cout << StringPrintf(" (%d horizontal, %d vertical)",
horizontal_lines.size(), vertical_lines.size())
<< std::endl;
std::cout << "Estimating vanishing points...";
RANSACOptions ransac_options;
ransac_options.max_error = options.max_line_vp_distance;
RANSAC<VanishingPointEstimator> ransac(ransac_options);
const auto horizontal_report =
ransac.Estimate(horizontal_line_segments, horizontal_lines);
const auto vertical_report =
ransac.Estimate(vertical_line_segments, vertical_lines);
std::cout << StringPrintf(" (%d horizontal inliers, %d vertical inliers)",
horizontal_report.support.num_inliers,
vertical_report.support.num_inliers)
<< std::endl;
std::cout << "Composing coordinate axes..." << std::endl;
const Eigen::Matrix3d inv_calib_matrix =
undistorted_camera.CalibrationMatrix().inverse();
const Eigen::Vector4d inv_qvec = InvertQuaternion(image.Qvec());
if (horizontal_report.success) {
const Eigen::Vector3d horizontal_camera_axis =
(inv_calib_matrix * horizontal_report.model).normalized();
Eigen::Vector3d horizontal_axis =
QuaternionRotatePoint(inv_qvec, horizontal_camera_axis).normalized();
// Make sure all axes point into the same direction.
if (rightward_axes.size() > 0 &&
rightward_axes[0].dot(horizontal_axis) < 0) {
horizontal_axis = -horizontal_axis;
}
rightward_axes.push_back(horizontal_axis);
std::cout << " Horizontal: " << horizontal_axis.transpose() << std::endl;
}
if (vertical_report.success) {
const Eigen::Vector3d vertical_camera_axis =
(inv_calib_matrix * vertical_report.model).normalized();
Eigen::Vector3d vertical_axis =
QuaternionRotatePoint(inv_qvec, vertical_camera_axis).normalized();
// Make sure axis points downwards in the image, assuming that the image
// was taken in upright orientation.
if (vertical_camera_axis.dot(Eigen::Vector3d(0, 1, 0)) < 0) {
vertical_axis = -vertical_axis;
}
downward_axes.push_back(vertical_axis);
std::cout << " Vertical: " << vertical_axis.transpose() << std::endl;
}
}
PrintHeading1("Computing coordinate frame");
Eigen::Matrix3d frame = Eigen::Matrix3d::Zero();
if (rightward_axes.size() > 0) {
frame.col(0) =
FindBestConsensusAxis(rightward_axes, options.max_axis_distance);
}
std::cout << "Found rightward axis: " << frame.col(0).transpose()
<< std::endl;
if (downward_axes.size() > 0) {
frame.col(1) =
FindBestConsensusAxis(downward_axes, options.max_axis_distance);
}
std::cout << "Found downward axis: " << frame.col(1).transpose() << std::endl;
if (rightward_axes.size() > 0 && downward_axes.size() > 0) {
frame.col(2) = frame.col(0).cross(frame.col(1));
Eigen::JacobiSVD<Eigen::Matrix3d> svd(
frame, Eigen::ComputeFullV | Eigen::ComputeFullU);
const Eigen::Matrix3d orthonormal_frame =
svd.matrixU() * Eigen::Matrix3d::Identity() * svd.matrixV().transpose();
frame = orthonormal_frame;
}
std::cout << "Found orthonormal frame: " << std::endl;
std::cout << frame << std::endl;
return frame;
}
void AlignToPrincipalPlane(Reconstruction* recon, SimilarityTransform3* tform) {
// Perform SVD on the 3D points to estimate the ground plane basis
const Eigen::Vector3d centroid = recon->ComputeCentroid(0.0, 1.0);
Eigen::MatrixXd points(3, recon->NumPoints3D());
int pidx = 0;
for (const auto& point : recon->Points3D()) {
points.col(pidx++) = point.second.XYZ() - centroid;
}
const Eigen::Matrix3d basis =
points.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).matrixU();
Eigen::Matrix3d rot_mat;
rot_mat << basis.col(0), basis.col(1), basis.col(0).cross(basis.col(1));
rot_mat.transposeInPlace();
*tform = SimilarityTransform3(1.0, RotationMatrixToQuaternion(rot_mat),
-rot_mat * centroid);
// if camera plane ends up below ground then flip basis vectors and create new
// transform
Image test_img = recon->Images().begin()->second;
tform->TransformPose(&test_img.Qvec(), &test_img.Tvec());
if (test_img.ProjectionCenter().z() < 0.0) {
rot_mat << basis.col(0), -basis.col(1), basis.col(0).cross(-basis.col(1));
rot_mat.transposeInPlace();
*tform = SimilarityTransform3(1.0, RotationMatrixToQuaternion(rot_mat),
-rot_mat * centroid);
}
recon->Transform(*tform);
}
void AlignToENUPlane(Reconstruction* recon, SimilarityTransform3* tform,
bool unscaled) {
const Eigen::Vector3d centroid = recon->ComputeCentroid(0.0, 1.0);
GPSTransform gps_tform;
const Eigen::Vector3d ell_centroid = gps_tform.XYZToEll({centroid}).at(0);
// Create rotation matrix from ECEF to ENU coordinates
const double sin_lat = sin(DegToRad(ell_centroid(0)));
const double sin_lon = sin(DegToRad(ell_centroid(1)));
const double cos_lat = cos(DegToRad(ell_centroid(0)));
const double cos_lon = cos(DegToRad(ell_centroid(1)));
// Create ECEF to ENU rotation matrix
Eigen::Matrix3d rot_mat;
rot_mat << -sin_lon, cos_lon, 0, -cos_lon * sin_lat, -sin_lon * sin_lat,
cos_lat, cos_lon * cos_lat, sin_lon * cos_lat, sin_lat;
const double scale = unscaled ? 1.0 / tform->Scale() : 1.0;
*tform = SimilarityTransform3(scale, RotationMatrixToQuaternion(rot_mat),
-(scale * rot_mat) * centroid);
recon->Transform(*tform);
}
} // namespace colmap