vtk vtkIterativeClosestPointTransform 点云数据配准最经典的方法是迭代最近点算法 ICP

     点云数据配准最经典的方法是迭代最近点算法（Iterative Closest Points,ICP）。ICP算法是一个迭代的过程，每次迭代中对于源数据点P找到目标点集Q中的最近点，然后给予最小二乘原理求解当前的变换矩阵T。通过不断迭代迭代直至收敛，即完成了点集的配准;

vtkSmartPointer<vtkDICOMImageReader>  QtMyShow::Read_VTK_DICOM_DIR(const char * path)
{
	vtkSmartPointer<vtkDICOMImageReader> reader =
		vtkSmartPointer<vtkDICOMImageReader>::New();

	reader->SetDirectoryName(path);
	reader->Update();

	return reader;

}

vtkSmartPointer<vtkPolyData>  QtMyShow::ReadDicom(const char * path)
{
	vtkSmartPointer<vtkDICOMImageReader> reader =
		Read_VTK_DICOM_DIR(path);
 

		//抽取等值面为骨头的信息
	vtkMarchingCubes *boneExtractor = vtkMarchingCubes::New();
	boneExtractor->SetInputData(reader->GetOutput());
	boneExtractor->SetValue(0, 85); //设置提取的等值信息
	boneExtractor->Update();
 
 剔除旧的或废除的数据单元，提高绘制速度
	//vtkStripper *boneStripper = vtkStripper::New(); //三角带连接                                     
	//boneStripper->SetInputData(boneExtractor->GetOutput());
	//boneStripper->Update();

	vtkSmartPointer<vtkPolyData> data = vtkSmartPointer<vtkPolyData>::New();
	data->DeepCopy(boneExtractor->GetOutput());
	return data;
	//return reader->GetOutput();
}

int main()
{
  vtkSmartPointer<vtkVertexGlyphFilter>  sourceGlyph =
		vtkSmartPointer<vtkVertexGlyphFilter>::New();
	//sourceGlyph->SetInputData(ReadDicom("E:/2"));
	sourceGlyph->SetInputData(ReadDicom("E:/2"));
	sourceGlyph->Update();
 
	vtkSmartPointer<vtkTransform> trans =
		vtkSmartPointer<vtkTransform>::New();
	trans->Translate(0.2, 0.1, 0.1);
	trans->RotateX(-30);

	vtkSmartPointer<vtkTransformPolyDataFilter> transformFilter1 =
		vtkSmartPointer<vtkTransformPolyDataFilter>::New();
	transformFilter1->SetInputData(sourceGlyph->GetOutput());
	transformFilter1->SetTransform(trans);
	transformFilter1->Update();
 


	target ->DeepCopy(sourceGlyph->GetOutput());
	source->DeepCopy(transformFilter1->GetOutput());

    vtkNew<vtkIterativeClosestPointTransform> icp;
	icp->SetSource(source);
	icp->SetTarget(target);
	icp->GetLandmarkTransform()->SetModeToRigidBody();
	
	icp->SetMaximumNumberOfIterations(50);
	icp->StartByMatchingCentroidsOn();
	icp->Modified();
	icp->Update();

 
	// Get the resulting transformation matrix (this matrix takes the source
	// points to the target points)
	vtkSmartPointer<vtkMatrix4x4> m = icp->GetMatrix();
	std::cout << "The resulting matrix is: " << *m << std::endl;

	// Transform the source points by the ICP solution
	vtkNew<vtkTransformPolyDataFilter> icpTransformFilter;
	icpTransformFilter->SetInputData(source);
	icpTransformFilter->SetTransform(icp);
	icpTransformFilter->Update();
}

我测试了一下，对于同源的数据可能效果不错，不过对于异源数据，多模数据，效果很差；