Clarified "normalised"/"unnormalised" by using "orthonormal" in BFM conversion

Both the BFM and SFM store the orthonormal basis in their files. The orthonormal basis is what eos calls "unnormalised" (because it's not rescaled by the eigenvalues).
We will get rid of this ambiguous terminology in eos now and use the unambiguous terms:

- Orthonormal basis (previously "unnormalised")
- Rescaled basis (previously "normalised).

share/convert_bfm2009_to_raw_binary.m

@@ -38,7 +38,7 @@ fwrite(f, size(bfm.shapePC, 2), 'int32'); % number of basis vectors
 % Write the shape mean:
 fwrite(f, bfm.shapeMU, 'float');
 
-% Write the unnormalised shape PCA basis matrix:
+% Write the orthonormal shape PCA basis matrix:
 % All of basis 1 will be written first, then basis 2, etc.
 fwrite(f, bfm.shapePC, 'float');
 
@@ -56,7 +56,7 @@ fwrite(f, size(bfm.texPC, 2), 'int32'); % number of basis vectors
 % Write the colour mean:
 fwrite(f, bfm.texMU, 'float');
 
-% Write the unnormalised colour PCA basis matrix:
+% Write the orthonormal colour PCA basis matrix:
 % All of basis 1 will be written first, then basis 2, etc.
 fwrite(f, bfm.texPC, 'float');
 

utils/bfm-binary-to-cereal.cpp

@@ -140,14 +140,14 @@ int main(int argc, char *argv[])
 		mean_shape(i) = value;
 	}
 
-	// Read the unnormalised shape basis matrix:
-	MatrixXf unnormalised_pca_basis_shape(num_vertices * 3, num_shape_basis_vectors); // m x n (rows x cols) = numShapeDims x numShapePcaCoeffs
-	std::cout << "Loading shape PCA basis matrix with " << unnormalised_pca_basis_shape.rows() << " rows and " << unnormalised_pca_basis_shape.cols() << " cols." << std::endl;
+	// Read the orthonormal shape basis matrix:
+	MatrixXf orthonormal_pca_basis_shape(num_vertices * 3, num_shape_basis_vectors); // m x n (rows x cols) = numShapeDims x numShapePcaCoeffs
+	std::cout << "Loading shape PCA basis matrix with " << orthonormal_pca_basis_shape.rows() << " rows and " << orthonormal_pca_basis_shape.cols() << " cols." << std::endl;
 	for (int col = 0; col < num_shape_basis_vectors; ++col) {
 		for (int row = 0; row < num_vertices * 3; ++row) {
 			float value = 0.0f;
 			file.read(reinterpret_cast<char*>(&value), 4);
-			unnormalised_pca_basis_shape(row, col) = value;
+			orthonormal_pca_basis_shape(row, col) = value;
 		}
 	}
 
@@ -177,9 +177,9 @@ int main(int argc, char *argv[])
 		triangle_list[i][2] = v2 - 1;
 	}
 
-	// We read the unnormalised basis from the file. Now let's normalise it and store the normalised basis separately.
-	auto normalised_pca_basis_shape = morphablemodel::normalise_pca_basis(unnormalised_pca_basis_shape, eigenvalues_shape);
-	morphablemodel::PcaModel shape_model(mean_shape, normalised_pca_basis_shape, eigenvalues_shape, triangle_list);
+	// We read the orthonormal basis from the file. Now let's rescale it and store the rescaled basis separately.
+	const auto rescaled_pca_basis_shape = morphablemodel::normalise_pca_basis(orthonormal_pca_basis_shape, eigenvalues_shape);
+	morphablemodel::PcaModel shape_model(mean_shape, rescaled_pca_basis_shape, eigenvalues_shape, triangle_list);
 
 	// Reading the colour (albedo) model:
 	int num_vertices_color = 0;
@@ -206,14 +206,14 @@ int main(int argc, char *argv[])
 		mean_color(i) = value / 255.0f;
 	}
 
-	// Read the unnormalised colour basis matrix:
-	MatrixXf unnormalised_pca_basis_color(num_vertices_color * 3, num_color_basis_vectors); // m x n (rows x cols) = num_colour_dims x num_colour_bases
-	std::cout << "Loading colour PCA basis matrix with " << unnormalised_pca_basis_color.rows() << " rows and " << unnormalised_pca_basis_color.cols() << " cols." << std::endl;
+	// Read the orthonormal colour basis matrix:
+	MatrixXf orthonormal_pca_basis_color(num_vertices_color * 3, num_color_basis_vectors); // m x n (rows x cols) = num_colour_dims x num_colour_bases
+	std::cout << "Loading colour PCA basis matrix with " << orthonormal_pca_basis_color.rows() << " rows and " << orthonormal_pca_basis_color.cols() << " cols." << std::endl;
 	for (int col = 0; col < num_color_basis_vectors; ++col) {
 		for (int row = 0; row < num_vertices_color * 3; ++row) {
 			float value = 0.0f;
 			file.read(reinterpret_cast<char*>(&value), 4);
-			unnormalised_pca_basis_color(row, col) = value;
+			orthonormal_pca_basis_color(row, col) = value;
 		}
 	}
 
@@ -225,9 +225,9 @@ int main(int argc, char *argv[])
 		eigenvalues_color(i) = value;
 	}
 
-	// We read the unnormalised basis from the file. Now let's normalise it and store the normalised basis separately.
-	auto normalised_pca_basis_color = morphablemodel::normalise_pca_basis(unnormalised_pca_basis_color, eigenvalues_color);
-	morphablemodel::PcaModel color_model(mean_color, normalised_pca_basis_color, eigenvalues_color, triangle_list);
+	// We read the orthonormal basis from the file. Now let's rescale it and store the rescaled basis separately.
+	const auto rescaled_pca_basis_color = morphablemodel::normalise_pca_basis(orthonormal_pca_basis_color, eigenvalues_color);
+	morphablemodel::PcaModel color_model(mean_color, rescaled_pca_basis_color, eigenvalues_color, triangle_list);
 
 	file.close();