Merge pull request #127 from ThibaultLejemble/fix-sphere-gradient

Fix sphere gradient (#118)
poncateam · Dec 15, 2023 · cfbe035 · cfbe035
2 parents 632e0e2 + dbf32e5
commit cfbe035
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Showing 3 changed files with 7 additions and 4 deletions.
diff --git a/CHANGELOG b/CHANGELOG
@@ -21,6 +21,7 @@ Current head (v.1.2 RC)
     - [spatialPartitioning] Fix potential compilation issues in KnnGraph (#111) 
     - [spatialPartitioning] Fix debug macros in KnnGraphRangeQuery (#121)
     - [fitting] Fix sphere fit eigen solver (#112)
+    - [fitting] Fix a bug in AlgebraicSphere::primtiveGradient (#127)
     - [spatialPartitioning] Improve kdtree to_string to output data in YAML format (#125)
 
 -Docs

diff --git a/Ponca/src/Fitting/algebraicSphere.h b/Ponca/src/Fitting/algebraicSphere.h
@@ -245,11 +245,13 @@ class AlgebraicSphere : public T
      */
     PONCA_MULTIARCH inline VectorType projectDescent (const VectorType& _q, int nbIter = 16) const;
 
-    //! \brief Approximation of the scalar field gradient at \f$ \mathbf{q} (not normalized) \f$
+    /*! \brief Approximation of the scalar field gradient at \f$ \mathbf{q}\f$
+        \warning The gradient is not normalized by default */
     PONCA_MULTIARCH inline VectorType primitiveGradient (const VectorType& _q) const;
 
-    /*! \brief Approximation of the scalar field gradient at the evaluation point */
-    PONCA_MULTIARCH inline VectorType primitiveGradient () const { return m_ul.normalized(); }
+    /*! \brief Approximation of the scalar field gradient at the evaluation point
+        \warning The gradient is not normalized by default */
+    PONCA_MULTIARCH inline const VectorType& primitiveGradient () const { return m_ul; }
 
     /*!
         \brief Used to know if the fitting result to a plane

diff --git a/tests/src/gls_paraboloid_der.cpp b/tests/src/gls_paraboloid_der.cpp
@@ -159,7 +159,7 @@ void testFunction(bool isSigned = true)
 //         dTau(k)    = ( f.tau()   - ref_fit.tau()   ) / h;
 
         dPotential(k) = ( flip_f*f.potential() - flip_ref * ref_fit.potential()   ) / h;
-        dN.col(k)     = ( flip_f*f.primitiveGradient()    - flip_ref * ref_fit.primitiveGradient()      ) / h;
+        dN.col(k)     = ( flip_f*f.primitiveGradient().normalized() - flip_ref * ref_fit.primitiveGradient().normalized() ) / h;
 //         dKappa(k)     = ( f.kappa() - ref_fit.kappa() ) / h;
       }