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Make canned loss functions more robust.

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The loss functions that ship with ceres can sometimes
generate a zero first derivative if the residual is too
large.

In such cases Corrector fails with an ugly undebuggable
crash. This CL is the first in a series of fixes to
take care of this.

We clamp the values of rho' from below by
numeric_limits<double>::min().

Also included here is some minor cleanup where the constants
are treated as doubles rather than integers.

Thanks to Pierre Moulon for reporting this problem.

Change-Id: I3aaf375303ecc2659bbf6fb56a812e7dc3a41106
This commit is contained in:
Sameer Agarwal
2014-07-30 10:14:15 -07:00
parent b27a2044b3
commit 1b17145adf
1 changed files with 17 additions and 17 deletions
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internal/ceres/loss_function.cc
+17 -17
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@@ -39,8 +39,8 @@ namespace ceres {
void TrivialLoss::Evaluate(double s, double rho[3]) const {
rho[0] = s;
rho[1] = 1;
rho[2] = 0;
rho[1] = 1.0;
rho[2] = 0.0;
}
void HuberLoss::Evaluate(double s, double rho[3]) const {
@@ -48,32 +48,32 @@ void HuberLoss::Evaluate(double s, double rho[3]) const {
// Outlier region.
// 'r' is always positive.
const double r = sqrt(s);
rho[0] = 2 * a_ * r - b_;
rho[1] = a_ / r;
rho[2] = - rho[1] / (2 * s);
rho[0] = 2.0 * a_ * r - b_;
rho[1] = std::max(std::numeric_limits<double>::min(), a_ / r);
rho[2] = - rho[1] / (2.0 * s);
} else {
// Inlier region.
rho[0] = s;
rho[1] = 1;
rho[2] = 0;
rho[1] = 1.0;
rho[2] = 0.0;
}
}
void SoftLOneLoss::Evaluate(double s, double rho[3]) const {
const double sum = 1 + s * c_;
const double sum = 1.0 + s * c_;
const double tmp = sqrt(sum);
// 'sum' and 'tmp' are always positive, assuming that 's' is.
rho[0] = 2 * b_ * (tmp - 1);
rho[1] = 1 / tmp;
rho[2] = - (c_ * rho[1]) / (2 * sum);
rho[0] = 2.0 * b_ * (tmp - 1.0);
rho[1] = std::max(std::numeric_limits<double>::min(), 1.0 / tmp);
rho[2] = - (c_ * rho[1]) / (2.0 * sum);
}
void CauchyLoss::Evaluate(double s, double rho[3]) const {
const double sum = 1 + s * c_;
const double inv = 1 / sum;
const double sum = 1.0 + s * c_;
const double inv = 1.0 / sum;
// 'sum' and 'inv' are always positive, assuming that 's' is.
rho[0] = b_ * log(sum);
rho[1] = inv;
rho[1] = std::max(std::numeric_limits<double>::min(), inv);
rho[2] = - c_ * (inv * inv);
}
@@ -82,8 +82,8 @@ void ArctanLoss::Evaluate(double s, double rho[3]) const {
const double inv = 1 / sum;
// 'sum' and 'inv' are always positive.
rho[0] = a_ * atan2(s, a_);
rho[1] = inv;
rho[2] = -2 * s * b_ * (inv * inv);
rho[1] = std::max(std::numeric_limits<double>::min(), inv);
rho[2] = -2.0 * s * b_ * (inv * inv);
}
TolerantLoss::TolerantLoss(double a, double b)
@@ -108,7 +108,7 @@ void TolerantLoss::Evaluate(double s, double rho[3]) const {
} else {
const double e_x = exp(x);
rho[0] = b_ * log(1.0 + e_x) - c_;
rho[1] = e_x / (1.0 + e_x);
rho[1] = std::max(std::numeric_limits<double>::min(), e_x / (1.0 + e_x));
rho[2] = 0.5 / (b_ * (1.0 + cosh(x)));
}
}