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Minor improvements to model scoring #23

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Minor improvements to model scoring #23

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20 changes: 14 additions & 6 deletions RansacLib/ransac.h
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Original file line number Diff line number Diff line change
Expand Up @@ -259,7 +259,8 @@ class LocallyOptimizedMSAC : public RansacBase {
solver.LeastSquares(stats.inlier_indices, &refined_model);

double score = std::numeric_limits<double>::max();
ScoreModel(solver, refined_model, kSqrInlierThresh, &score);
ScoreModel(solver, refined_model, kSqrInlierThresh, &score,
stats.best_model_score);
if (score < stats.best_model_score) {
stats.best_model_score = score;
*best_model = refined_model;
Expand All @@ -283,7 +284,8 @@ class LocallyOptimizedMSAC : public RansacBase {
*best_model_id = 0;
for (int m = 0; m < num_models; ++m) {
double score = std::numeric_limits<double>::max();
ScoreModel(solver, models[m], squared_inlier_threshold, &score);
ScoreModel(solver, models[m], squared_inlier_threshold, &score,
*best_score);

if (score < *best_score) {
*best_score = score;
Expand All @@ -293,12 +295,16 @@ class LocallyOptimizedMSAC : public RansacBase {
}

void ScoreModel(const Solver& solver, const Model& model,
const double squared_inlier_threshold, double* score) const {
const double squared_inlier_threshold, double* score
const double best_score) const {
const int kNumData = solver.num_data();
*score = 0.0;
for (int i = 0; i < kNumData; ++i) {
double squared_error = solver.EvaluateModelOnPoint(model, i);
*score += ComputeScore(squared_error, squared_inlier_threshold);
if( *score > best_score) {
return;
}
}
}

Expand Down Expand Up @@ -361,7 +367,7 @@ class LocallyOptimizedMSAC : public RansacBase {
LeastSquaresFit(options, kSqInThresh * kThreshMult, solver, rng, &m_init);

double score = std::numeric_limits<double>::max();
ScoreModel(solver, m_init, kSqInThresh, &score);
ScoreModel(solver, m_init, kSqInThresh, &score, score_best_minimal_model);
UpdateBestModel(score, m_init, score_best_minimal_model,
best_minimal_model);

Expand All @@ -383,7 +389,8 @@ class LocallyOptimizedMSAC : public RansacBase {
Model m_non_min;
if (!solver.NonMinimalSolver(sample, &m_non_min)) continue;

ScoreModel(solver, m_non_min, kSqInThresh, &score);
ScoreModel(solver, m_non_min, kSqInThresh, &score,
score_best_minimal_model);
UpdateBestModel(score, m_non_min, score_best_minimal_model,
best_minimal_model);

Expand All @@ -398,7 +405,8 @@ class LocallyOptimizedMSAC : public RansacBase {
for (int i = 0; i < options.num_lsq_iterations_; ++i) {
LeastSquaresFit(options, thresh, solver, rng, &m_non_min);

ScoreModel(solver, m_non_min, kSqInThresh, &score);
ScoreModel(solver, m_non_min, kSqInThresh, &score,
score_best_minimal_model);
UpdateBestModel(score, m_non_min, score_best_minimal_model,
best_minimal_model);
thresh -= thresh_mult_update;
Expand Down