diff --git a/apps/sample/trimesh_ransac/trimesh_ransac.cpp b/apps/sample/trimesh_ransac/trimesh_ransac.cpp
index 6397a922..1ceef66a 100644
--- a/apps/sample/trimesh_ransac/trimesh_ransac.cpp
+++ b/apps/sample/trimesh_ransac/trimesh_ransac.cpp
@@ -29,6 +29,8 @@
 #include<vcg/complex/algorithms/update/color.h>
 #include<vcg/complex/algorithms/inertia.h>
 #include<vcg/complex/algorithms/point_sampling.h>
+#include<vcg/complex/algorithms/ransac_matching.h>
+
 #include<vcg/space/index/kdtree/kdtree.h>
 #include<vcg/space/point_matching.h>
 
@@ -46,415 +48,6 @@ class MyEdge    : public Edge<   MyUsedTypes, edge::VertexRef, edge::VEAdj, edge
 class MyMesh    : public tri::TriMesh< std::vector<MyVertex>, std::vector<MyFace> , std::vector<MyEdge>  > {};
 
 
-
-template <class MeshType> 
-class BaseFeature
-{
-public:
-  BaseFeature():_v(0) {}
-  typename MeshType::VertexType *_v;  
-  typename MeshType::CoordType P() {return _v->cP();}    
-};
-template <class MeshType>
-class BaseFeatureSet 
-{
-public: 
-  typedef   BaseFeature<MeshType> FeatureType;  
-  typedef typename MeshType::VertexType      VertexType;
- 
-  std::vector<FeatureType> fixFeatureVec;
-  std::vector<FeatureType> movFeatureVec;
-  
-  FeatureType &ff(int i) { return fixFeatureVec[i]; }
-  FeatureType &mf(int i) { return movFeatureVec[i]; }
-  int ffNum() const { return fixFeatureVec.size(); }
-  
-  void Init(MeshType &fix, MeshType &mov,  
-            std::vector<VertexType *> &fixSampleVec, std::vector<VertexType *> &movSampleVec)
-  {
-    this->fixFeatureVec.resize(fixSampleVec.size()/20);
-    for(int i=0;i<fixSampleVec.size()/20;++i) 
-      this->fixFeatureVec[i]._v = fixSampleVec[i];
-      
-    this->movFeatureVec.resize(movSampleVec.size()/20);
-    for(int i=0;i<movSampleVec.size()/20;++i) 
-      this->movFeatureVec[i]._v = movSampleVec[i];
-    
-    printf("Generated %i Features on Fix\n",this->fixFeatureVec.size());
-    printf("Generated %i Features on Mov\n",this->movFeatureVec.size());
-  }
- 
- // Returns the indexes of all the fix features matching a given one (from mov usually) 
- void getMatchingFeatureVec(FeatureType &q, vector<int> &mfiVec)
- {
-  mfiVec.resize(movFeatureVec.size());
-  
-  for(int i=0;i<movFeatureVec.size();++i)
-    mfiVec[i]=i;
- } 
-};
-
-
-template <class MeshType> 
-class NDFeature 
-{
-public:
-  typedef typename MeshType::ScalarType      ScalarType;
-  
-  typename MeshType::VertexType *_v;  
-  typename MeshType::CoordType nd; //  
-  
-  typename MeshType::CoordType P() {return _v->cP();}    
-  
-  static void EvalNormalVariation(MeshType &m, ScalarType dist)
-  {
-    tri::UpdateNormal<MeshType>::PerVertexNormalized(m);
-    
-    VertexConstDataWrapper<MeshType > ww(m);
-    KdTree<ScalarType> tree(ww); 
-      
-    for(int i=0;i<m.vn;++i)
-    {
-      std::vector<unsigned int> ptIndVec;
-      std::vector<ScalarType> sqDistVec;    
-      tree.doQueryDist(m.vert[i].P(),dist, ptIndVec, sqDistVec);
-      ScalarType varSum=0;
-      for(int j=0;j<sqDistVec.size();++j)
-      {
-        varSum += Distance(m.vert[i].N(),m.vert[ptIndVec[j]].N()); 
-      }
-     m.vert[i].Q()=varSum/ScalarType(ptIndVec.size());
-    }  
-    tri::UpdateColor<MeshType>::PerVertexQualityGray(m,0,0);
-  }
-  
-};
-
-
-
-
-
-
-
-
-template <class MeshType, class FeatureSetType>
-class RansacFramework
-{
-  typedef typename FeatureSetType::FeatureType       FeatureType;
-  typedef typename MeshType::CoordType       CoordType;
-  typedef typename MeshType::BoxType         BoxType;
-  typedef typename MeshType::ScalarType      ScalarType;
-  typedef typename MeshType::VertexType      VertexType;
-  typedef typename MeshType::VertexPointer   VertexPointer;
-  typedef typename MeshType::VertexIterator  VertexIterator;
-  typedef typename MeshType::EdgeType        EdgeType;
-  typedef typename MeshType::EdgeIterator    EdgeIterator;
-  typedef typename MeshType::FaceType        FaceType;
-  typedef typename MeshType::FacePointer     FacePointer;
-  typedef typename MeshType::FaceIterator    FaceIterator;
-  typedef typename MeshType::FaceContainer   FaceContainer;
-  typedef Matrix44<ScalarType>               Matrix44Type;
-  
-public:
-  class Param
-  {
-  public:
-    Param()
-    {
-      iterMax=100;
-      samplingRadiusPerc=0.005;
-      samplingRadiusAbs=0;
-      evalSize=1000;
-      inlierRatioThr=0.3;
-      inlierDistanceThrPerc = 1.5; // the distance between a transformed mov sample and the corresponding on fix should be 1.5 * sampling dist.
-      congruenceThrPerc = 2.0; // the distance between two matching features must be  within 2.0 * sampling distance 
-      minFeatureDistancePerc = 4.0; // the distance between two chosen features must be  at least 4.0 * sampling distance 
-    }
-   
-    ScalarType inlierRatioThr;
-    ScalarType inlierDistanceThrPerc;
-    ScalarType congruenceThrPerc;
-    ScalarType minFeatureDistancePerc;
-    ScalarType samplingRadiusPerc;
-    ScalarType samplingRadiusAbs;
-    int iterMax;
-    int evalSize;
-    
-    ScalarType inlierSquareThr() const { return pow(samplingRadiusAbs* inlierDistanceThrPerc,2); }
-  };
-  
-  class Candidate
-  {
-  public:
-    int fixInd[3];
-    int movInd[3];  
-    int inlierNum;
-    int evalSize;
-    Matrix44Type Tr;
-    ScalarType err() const {return float(inlierNum)/float(evalSize);}
-    bool operator <(const Candidate &cc) const
-    {
-      return this->err() > cc.err();
-    }
-    
-  };
-
-  FeatureSetType FS;
-  std::vector<Point3f> fixConsensusVec, movConsensusVec;
-  KdTree<ScalarType> *consensusTree;
-  
-  
-  // Given three pairs of sufficiently different distances (e.g. the edges of a scalene triangle)
-  // it finds the permutation that brings the vertexes so that the distances match.
-  // The meaning of the permutation vector nm0,nm1,nm2 is that the (N)ew index of (M)ov vertx i is the value of nmi 
-  
-  bool FindPermutation(int d01, int d02, int d12, int m01, int m02, int m12, int nm[], Param &pp)
-  {
-    ScalarType eps = pp.samplingRadiusAbs*2.0;
-        
-    if(fabs(d01-m01)<eps) {
-      if(fabs(d02-m02)<eps) {
-        if(fabs(d12-m12)<eps){ nm[0]=0;nm[1]=1;nm[2]=2; return true; }
-            else return false;
-      }
-      if(fabs(d02-m12)<eps) {
-        if(fabs(d12-m02)<eps){ nm[0]=1;nm[1]=0;nm[2]=2; return true; }
-            else return false;        
-      }        
-    }
-    
-    if(fabs(d01-m02)<eps) {
-      if(fabs(d02-m01)<eps) {
-        if(fabs(d12-m12)<eps){ nm[0]=0;nm[1]=2;nm[2]=1; return true; }
-            else return false;
-      }
-      if(fabs(d02-m12)<eps) {
-        if(fabs(d12-m01)<eps){ nm[0]=2;nm[1]=0;nm[2]=1; return true; }
-            else return false;        
-      }        
-    }
-
-    if(fabs(d01-m12)<eps) {
-      if(fabs(d02-m01)<eps) {
-        if(fabs(d12-m02)<eps){ nm[0]=1;nm[1]=2;nm[2]=0; return true; }
-            else return false;
-      }
-      if(fabs(d02-m02)<eps) {
-        if(fabs(d12-m01)<eps){ nm[0]=2;nm[1]=1;nm[2]=0; return true; }
-            else return false;        
-      }        
-    }
-    return false;
-  }
-    
-  // The main loop. 
-  // Choose three points on fix that make a scalene triangle 
-  // and search on mov three other points with matchng distances 
-  
-  void Process_SearchEvaluateTriple (vector<Candidate> &cVec, Param &pp)
-  {
-    math::MarsenneTwisterRNG rnd;
-//    ScalarType congruenceEps = pow(pp.samplingRadiusAbs * pp.congruenceThrPerc,2.0f);
-    ScalarType congruenceEps = pp.samplingRadiusAbs * pp.congruenceThrPerc;
-    ScalarType minFeatureDistEps = pp.samplingRadiusAbs * pp.minFeatureDistancePerc;
-    printf("Starting search congruenceEps = samplingRadiusAbs * 3.0 = %6.2f \n",congruenceEps);
-    int iterCnt=0;
-    
-    while ( (iterCnt < pp.iterMax) && (cVec.size()<100) )
-    {
-      Candidate c;
-      // Choose a random pair of features from fix 
-      c.fixInd[0] = rnd.generate(FS.ffNum());
-      c.fixInd[1] = rnd.generate(FS.ffNum());
-      ScalarType d01 = Distance(FS.ff(c.fixInd[0]).P(),FS.ff(c.fixInd[1]).P());
-      if( d01 > minFeatureDistEps )
-      {
-        c.fixInd[2] = rnd.generate(FS.ffNum());
-        ScalarType d02=Distance(FS.ff(c.fixInd[0]).P(),FS.ff(c.fixInd[2]).P());
-        ScalarType d12=Distance(FS.ff(c.fixInd[1]).P(),FS.ff(c.fixInd[2]).P());
-          
-        if( ( d02 > minFeatureDistEps ) &&  // Sample are sufficiently distant
-            ( d12 > minFeatureDistEps ) && 
-            ( fabs(d01-d02) > congruenceEps ) && // and they make a scalene triangle
-            ( fabs(d01-d12) > congruenceEps ) && 
-            ( fabs(d12-d02) > congruenceEps ) )
-        {
-          // Find a congruent triple on mov 
-          printf("Starting search of a [%i] congruent triple for %4i %4i %4i - %6.2f %6.2f %6.2f\n",iterCnt,c.fixInd[0],c.fixInd[1],c.fixInd[2],d01,d02,d12);
-          // As a first Step we ask for three vectors of matching features;
-          
-          std::vector<int> movFeatureVec0; 
-          FS.getMatchingFeatureVec(FS.ff(c.fixInd[0]), movFeatureVec0);
-          std::vector<int> movFeatureVec1; 
-          FS.getMatchingFeatureVec(FS.ff(c.fixInd[1]), movFeatureVec1);
-          std::vector<int> movFeatureVec2; 
-          FS.getMatchingFeatureVec(FS.ff(c.fixInd[2]), movFeatureVec2);
-          
-          int congrNum=0;
-          int congrGoodNum=0;
-          for(int i=0;i<movFeatureVec0.size();++i)
-          { 
-            if(cVec.size()>100) break;
-            c.movInd[0]=movFeatureVec0[i];
-            for(int j=0;j<movFeatureVec1.size();++j)
-            {               
-              if(cVec.size()>100) break;             
-              c.movInd[1]=movFeatureVec1[j];              
-              ScalarType m01 = Distance(FS.mf(c.movInd[0]).P(),FS.mf(c.movInd[1]).P());
-              if( (fabs(m01-d01)<congruenceEps) )
-              {
-//                printf("- Found a congruent pair %i %i %6.2f\n", c.movInd[0],c.movInd[1], m01);                
-                ++congrNum;
-                for(int k=0;k<movFeatureVec2.size();++k)
-                { 
-                  if(cVec.size()>100) break;                  
-                  c.movInd[2]=movFeatureVec2[k];
-                  ScalarType m02=Distance(FS.mf(c.movInd[0]).P(),FS.mf(c.movInd[2]).P());
-                  ScalarType m12=Distance(FS.mf(c.movInd[1]).P(),FS.mf(c.movInd[2]).P());
-                  if( (fabs(m02-d02)<congruenceEps)  && (fabs(m12-d12)<congruenceEps ) )
-                  {
-                    c.Tr = GenerateMatchingMatrix(c,pp);
-                    
-                    EvaluateMatrix(c,pp);
-                    if(c.err() > pp.inlierRatioThr ){
-                      printf("- - Found  %i th good congruent triple %i %i %i -- %f / %i \n", cVec.size(), c.movInd[0],c.movInd[1],c.movInd[2],c.err(),pp.evalSize);
-                      ++congrGoodNum;                      
-                      cVec.push_back(c);
-                    }
-                  }
-                }
-              }                
-            }
-          }
-          printf("Completed Search of congruent triple (found %i / %i good/congruent)\n",congrGoodNum,congrNum);
-        }               
-      }
-      ++iterCnt;
-    } // end While
-
-    printf("Found %i candidates \n",cVec.size());
-    sort(cVec.begin(),cVec.end());
-    printf("best candidate %f \n",cVec[0].err());
-    
-    pp.evalSize = pp.evalSize*10;
-    
-    for(int i=0;i<cVec.size();++i)
-      EvaluateMatrix(cVec[i],pp);
-    
-    sort(cVec.begin(),cVec.end());
-    
-    printf("After re-evaluation best is %f",cVec[0].err());
-        
-      
-    
-  } // end Process
-  
-  int EvaluateMatrix(Candidate &c, Param &pp)
-  {
-    c.inlierNum=0;
-    c.evalSize=pp.evalSize;    
-    
-    ScalarType sqThr = pp.inlierSquareThr();
-    Distribution<ScalarType> H;
-    for(int i=0;i<pp.evalSize;++i)
-    {
-      Point3f qp = c.Tr*movConsensusVec[i];
-      uint ind;
-      ScalarType squareDist;
-      consensusTree->doQueryClosest(qp,ind,squareDist);
-      if(squareDist < sqThr)
-        ++c.inlierNum;
-    }
-  }
-  
-  int DumpInlier(MeshType &m, Candidate &c, Param &pp)
-  {
-    ScalarType sqThr = pp.inlierSquareThr();
-    for(int i=0;i<pp.evalSize;++i)
-    {
-      Point3f qp = c.Tr*movConsensusVec[i];
-      uint ind;
-      ScalarType squareDist;
-      consensusTree->doQueryClosest(qp,ind,squareDist);
-      if(squareDist < sqThr)
-        tri::Allocator<MyMesh>::AddVertex(m,qp);
-    }
-    
-  }
-  
-
-// Find the transformation that matches the mov onto the fix
-// eg M * piMov = piFix 
-
-Matrix44f GenerateMatchingMatrix(Candidate &c, Param pp)
-{
-  std::vector<Point3f> pFix(3);
-  pFix[0]= FS.ff(c.fixInd[0]).P();
-  pFix[1]= FS.ff(c.fixInd[1]).P();
-  pFix[2]= FS.ff(c.fixInd[2]).P();
-  
-  std::vector<Point3f> pMov(3);
-  pMov[0]= FS.mf(c.movInd[0]).P();
-  pMov[1]= FS.mf(c.movInd[1]).P();
-  pMov[2]= FS.mf(c.movInd[2]).P();
-
-  Point3f upFix = vcg::Normal(pFix[0],pFix[1],pFix[2]);
-  Point3f upMov = vcg::Normal(pMov[0],pMov[1],pMov[2]);  
-  
-  upFix.Normalize(); 
-  upMov.Normalize();
-  
-  upFix *= Distance(pFix[0],pFix[1]);
-  upMov *= Distance(pMov[0],pMov[1]);
-  
-  for(int i=0;i<3;++i) pFix.push_back(pFix[i]+upFix);
-  for(int i=0;i<3;++i) pMov.push_back(pMov[i]+upMov);
-  
-  Matrix44f res;
-  ComputeRigidMatchMatrix(pFix,pMov,res);
-  return res;  
-}
-
-
-void Init(MeshType &fixM, MeshType &movM, Param &pp)
-{
-  // First a bit of Sampling
-  typedef tri::TrivialPointerSampler<MeshType> BaseSampler;
-  typename tri::SurfaceSampling<MeshType, BaseSampler>::PoissonDiskParam pdp;
-  pdp.randomSeed = 0;
-  pdp.bestSampleChoiceFlag = true;
-  pdp.bestSamplePoolSize = 20;
-  int t0=clock();
-  pp.samplingRadiusAbs = pp.samplingRadiusPerc *fixM.bbox.Diag();
-  BaseSampler pdSampler;
-  std::vector<VertexType *> fixSampleVec;
-  tri::SurfaceSampling<MeshType,BaseSampler>::PoissonDiskPruning(pdSampler, fixM, pp.samplingRadiusAbs,pdp);
-  std::swap(pdSampler.sampleVec,fixSampleVec);
-  std::vector<VertexType *> movSampleVec;  
-  tri::SurfaceSampling<MeshType,BaseSampler>::PoissonDiskPruning(pdSampler, movM, pp.samplingRadiusAbs,pdp);
-  std::swap(pdSampler.sampleVec,movSampleVec);
-  int t1=clock();
-  printf("Poisson Sampling of surfaces %5.2f ( %iv and %iv) \n",float(t1-t0)/CLOCKS_PER_SEC,fixSampleVec.size(),movSampleVec.size());
-  printf("Sampling Radius %f \n",pp.samplingRadiusAbs);
-  
-  for(int i=0;i<fixSampleVec.size();++i) 
-    this->fixConsensusVec.push_back(fixSampleVec[i]->P());    
-
-  for(int i=0;i<movSampleVec.size();++i) 
-    this->movConsensusVec.push_back(movSampleVec[i]->P());
-  
-  FS.Init(fixM, movM, fixSampleVec, movSampleVec);
-    
-  std::random_shuffle(movConsensusVec.begin(),movConsensusVec.end());
-  
-  VectorConstDataWrapper<std::vector<CoordType> > ww(fixConsensusVec);
-  consensusTree = new  KdTree<ScalarType>(ww); 
-}
-
-
-};
-
-
 int main( int argc, char **argv )
 {
   setvbuf(stdout, NULL, _IONBF, 0);
diff --git a/apps/sample/trimesh_ransac/trimesh_ransac.pro b/apps/sample/trimesh_ransac/trimesh_ransac.pro
index 784b17ad..ab40fb73 100644
--- a/apps/sample/trimesh_ransac/trimesh_ransac.pro
+++ b/apps/sample/trimesh_ransac/trimesh_ransac.pro
@@ -1,4 +1,4 @@
 include(../common.pri)
-TARGET = ransac 
+TARGET = trimesh_ransac 
 SOURCES +=  ../../../wrap/ply/plylib.cpp \
-    ransac.cpp
+    trimesh_ransac.cpp