Revision: 46623
http://projects.blender.org/scm/viewvc.php?view=rev&root=bf-blender&revision=46623
Author: keir
Date: 2012-05-14 12:15:38 +0000 (Mon, 14 May 2012)
Log Message:
-----------
"Efficient Second-order Minimization" for the planar tracker
This implements the "Efficient Second-order Minimization"
scheme, as supported by the existing translation tracker.
This increases the amount of per-iteration work, but
decreases the number of iterations required to converge and
also increases the size of the basin of attraction for the
optimization.
Modified Paths:
--------------
branches/soc-2011-tomato/extern/libmv/libmv/tracking/esm_region_tracker.cc
branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.cc
branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.h
Modified:
branches/soc-2011-tomato/extern/libmv/libmv/tracking/esm_region_tracker.cc
===================================================================
--- branches/soc-2011-tomato/extern/libmv/libmv/tracking/esm_region_tracker.cc
2012-05-14 12:04:00 UTC (rev 46622)
+++ branches/soc-2011-tomato/extern/libmv/libmv/tracking/esm_region_tracker.cc
2012-05-14 12:15:38 UTC (rev 46623)
@@ -102,6 +102,7 @@
options.num_samples_y = 2 * half_window_size + 1;
options.max_iterations = 20;
options.sigma = sigma;
+ options.use_esm = true;
TrackRegionResult result;
TrackRegion(image1, image2, xx1, yy1, options, xx2, yy2, &result);
Modified: branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.cc
===================================================================
--- branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.cc
2012-05-14 12:04:00 UTC (rev 46622)
+++ branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.cc
2012-05-14 12:15:38 UTC (rev 46623)
@@ -63,33 +63,32 @@
// supported, the sample function must be inside a template-specialized class
// with a non-templated static member.
-// The "AutoDiffImage::Sample()" function allows sampling an image at an x, y
+// The "AutoDiff::Sample()" function allows sampling an image at an x, y
// position such that if x and y are jets, then the derivative information is
// correctly propagated.
// Empty default template.
template<typename T>
-struct AutoDiffImage {
+struct AutoDiff {
+ // Sample only the image when the coordinates are scalars.
static T Sample(const FloatImage &image_and_gradient,
const T &x, const T &y) {
- return 0.0;
+ return SampleLinear(image_and_gradient, y, x, 0);
}
-};
-// Sample only the image when the coordinates are scalars.
-template<>
-struct AutoDiffImage<double> {
- static double Sample(const FloatImage &image_and_gradient,
- const double& x, const double& y) {
- return SampleLinear(image_and_gradient, y, x, 0);
+ static void SetScalarPart(double scalar, T *value) {
+ *value = scalar;
}
+ static void ScaleDerivative(double scale_by, T *value) {
+ // For double, there is no derivative to scale.
+ }
};
// Sample the image and gradient when the coordinates are jets, applying the
// jacobian appropriately to propagate the derivatives from the coordinates.
template<>
template<typename T, int N>
-struct AutoDiffImage<ceres::Jet<T, N> > {
+struct AutoDiff<ceres::Jet<T, N> > {
static ceres::Jet<T, N> Sample(const FloatImage &image_and_gradient,
const ceres::Jet<T, N> &x,
const ceres::Jet<T, N> &y) {
@@ -116,6 +115,13 @@
jet_s.v = Matrix<T, 1, 2>(dsdx, dsdy) * dxydz;
return jet_s;
}
+
+ static void SetScalarPart(double scalar, ceres::Jet<T, N> *value) {
+ value->a = scalar;
+ }
+ static void ScaleDerivative(double scale_by, ceres::Jet<T, N> *value) {
+ value->v *= scale_by;
+ }
};
template<typename Warp>
@@ -151,18 +157,16 @@
template<typename Warp>
class WarpCostFunctor {
public:
- WarpCostFunctor(const FloatImage &image_and_gradient1,
+ WarpCostFunctor(const TrackRegionOptions &options,
+ const FloatImage &image_and_gradient1,
const FloatImage &image_and_gradient2,
const Mat3 &canonical_to_image1,
- const Warp &warp,
- int num_samples_x,
- int num_samples_y)
- : image_and_gradient1_(image_and_gradient1),
+ const Warp &warp)
+ : options_(options),
+ image_and_gradient1_(image_and_gradient1),
image_and_gradient2_(image_and_gradient2),
canonical_to_image1_(canonical_to_image1),
- warp_(warp),
- num_samples_x_(num_samples_x),
- num_samples_y_(num_samples_y) {}
+ warp_(warp) {}
template<typename T>
bool operator()(const T *warp_parameters, T *residuals) const {
@@ -171,8 +175,8 @@
}
int cursor = 0;
- for (int r = 0; r < num_samples_y_; ++r) {
- for (int c = 0; c < num_samples_x_; ++c) {
+ for (int r = 0; r < options_.num_samples_y; ++r) {
+ for (int c = 0; c < options_.num_samples_x; ++c) {
// Compute the location of the source pixel (via homography).
Vec3 image1_position = canonical_to_image1_ * Vec3(c, r, 1);
image1_position /= image1_position(2);
@@ -185,28 +189,52 @@
&image2_position[0],
&image2_position[1]);
- // Sample the source and destination.
- double src_sample = AutoDiffImage<double>::Sample(image_and_gradient1_,
- image1_position[0],
- image1_position[1]);
- T dst_sample = AutoDiffImage<T>::Sample(image_and_gradient2_,
- image2_position[0],
- image2_position[1]);
+ // Sample the destination, propagating derivatives.
+ T dst_sample = AutoDiff<T>::Sample(image_and_gradient2_,
+ image2_position[0],
+ image2_position[1]);
+
+ // Sample the source. This is made complicated by ESM mode.
+ T src_sample;
+ if (options_.use_esm) {
+ // In ESM mode, the derivative of the source is also taken into
+ // account. This changes the linearization in a way that causes
+ // better convergence. Copy the derivative of the warp parameters
+ // onto the jets for the image1 position. This is the ESM hack.
+ T image1_position_x = image2_position[0];
+ T image1_position_y = image2_position[1];
+ AutoDiff<T>::SetScalarPart(image1_position[0], &image1_position_x);
+ AutoDiff<T>::SetScalarPart(image1_position[1], &image1_position_y);
+ src_sample = AutoDiff<T>::Sample(image_and_gradient1_,
+ image1_position_x,
+ image1_position_y);
+
+ // The jacobians for these should be averaged. Due to the subtraction
+ // below, flip the sign of the src derivative so that the effect
+ // after subtraction of the jets is that they are averaged.
+ AutoDiff<T>::ScaleDerivative(-0.5, &src_sample);
+ AutoDiff<T>::ScaleDerivative(0.5, &dst_sample);
+ } else {
+ // This is the traditional, forward-mode KLT solution.
+ src_sample = T(AutoDiff<double>::Sample(image_and_gradient1_,
+ image1_position[0],
+ image1_position[1]));
+ }
+
// The difference is the error.
- residuals[cursor++] = T(src_sample) - dst_sample;
+ residuals[cursor++] = src_sample - dst_sample;
}
}
return true;
}
private:
+ const TrackRegionOptions &options_;
const FloatImage &image_and_gradient1_;
const FloatImage &image_and_gradient2_;
const Mat3 &canonical_to_image1_;
const Warp &warp_;
- int num_samples_x_;
- int num_samples_y_;
};
// Compute the warp from rectangular coordinates, where one corner is the
@@ -570,12 +598,11 @@
// Construct the warp cost function. AutoDiffCostFunction takes ownership.
WarpCostFunctor<Warp> *cost_function =
- new WarpCostFunctor<Warp>(image_and_gradient1,
+ new WarpCostFunctor<Warp>(options,
+ image_and_gradient1,
image_and_gradient2,
canonical_homography,
- warp,
- options.num_samples_x,
- options.num_samples_y);
+ warp);
// Construct the problem with a single residual.
ceres::Problem::Options problem_options;
Modified: branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.h
===================================================================
--- branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.h
2012-05-14 12:04:00 UTC (rev 46622)
+++ branches/soc-2011-tomato/extern/libmv/libmv/tracking/track_region.h
2012-05-14 12:15:38 UTC (rev 46623)
@@ -47,6 +47,9 @@
double minimum_correlation;
int max_iterations;
+
+ // Use the "Efficient Second-order Minimization" scheme. This increases
+ // convergence speed at the cost of more per-iteration work.
bool use_esm;
double sigma;
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