surface reconstruction - point cloud libraryoutline 1.introduction 2.plane detection problem...

Surface ReconstructionVincent Rabaud

November 03, 2011

Outline

1. Introduction

2. Plane DetectionProblem definitionPlane estimationHull refinementResults

3. Moving least squaresProblemGistResults

4. TriangulationMeshingResults

5. SmoothingVTK smootherPoisson

6. Conclusion

Vincent Rabaud / PCL :: Surface Reconstruction

Introduction

Wanted: surface/mesh from a point cloud Why ?I the world is not sparseI for better graphics/visualizationI for texture mappingI CAD modelsI compression


Introduction

In robotics:I object detectionI object grasping


Plane detection

We are given a noisy point cloud and we want to find the bestplane


Plane detection

1 // Define the RANSAC object2 pcl::SACSegmentation<pcl::PointXYZ> seg;3 // Define its coefficients4 seg.setOptimizeCoefficients (true);5 seg.setMethodType (pcl::SAC_RANSAC);6 // Specify the plane parameters7 seg.setModelType (pcl::SACMODEL_PLANE);8 seg.setDistanceThreshold (0.01);9 // Specify the input

10 seg.setInputCloud (cloud_filtered);11 // Segment12 pcl::ModelCoefficients::Ptr coefficients (new pcl::

ModelCoefficients);13 pcl::PointIndices::Ptr inliers (new pcl::

PointIndices);14 seg.segment (*inliers, *coefficients);


Hull refinement

1 // Create the projection structure2 pcl::ProjectInliers<pcl::PointXYZ> proj;3 proj.setModelType (pcl::SACMODEL_PLANE);4 proj.setInputCloud (cloud_filtered);5 proj.setModelCoefficients (coefficients);6 // Create the projected point cloud7 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected

(new pcl::PointCloud<pcl::PointXYZ>);8 proj.filter (*cloud_projected);9 // Create a Convex Hull representation of the

projected inliers10 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_hull (new

pcl::PointCloud<pcl::PointXYZ>);11 pcl::ConvexHull<pcl::PointXYZ> chull;12 chull.setInputCloud (cloud_projected);13 chull.setAlpha (0.1);14 chull.reconstruct (*cloud_hull);


Plane detection

What a nice convex hull !


Outline

1. Introduction





6. Conclusion


Problem

Noisy input cloud, normals are all over the place


Moving Least squares

Input noisy cloud is named: cloud

1 // Moving least square object2 pcl::MovingLeastSquares<pcl::PointXYZ, pcl::Normal>

mls;3 mls.setInputCloud (cloud);4 mls.setPolynomialFit (true);5 mls.setSearchRadius (0.03);6 // Define the tree to find the neighbors7 pcl::search::KdTree<pcl::PointXYZ>::Ptr tree8 (new pcl::search::KdTree<pcl::PointXYZ>);9 tree->setInputCloud (cloud);

10 mls.setSearchMethod (tree);


Performing the smoothing

1 // Define the output and the normals2 pcl::PointCloud<pcl::PointXYZ> mls_points;3 pcl::PointCloud<pcl::Normal>::Ptr mls_normals (new4 pcl::PointCloud<pcl::Normal> ());5 mls.setOutputNormals (mls_normals);6 // Compute the smoothed cloud7 mls.reconstruct (mls_points);8 // Extra: merge fields9 pcl::PointCloud<pcl::PointNormal> mls_cloud;

10 pcl::concatenateFields (mls_points, *mls_normals,mls_cloud);


Result 1


Result 2

And even more smoothing


Outline

1. Introduction





6. Conclusion


Meshing

I method based on growing neighborhoodsI connect neighbors and increase the neighborhoods untill

all points are connectedI works best for smooth regions


Code

1 // Initialize objects2 pcl::GreedyProjectionTriangulation<pcl::PointNormal>

gp3;3

4 // Set the maximum distance between connected points(maximum edge length)

5 gp3.setSearchRadius (0.025);6

7 // Set typical values for the parameters8 gp3.setMu(2.5);9 gp3.setMaximumNearestNeighbors(100);

10 gp3.setMaximumSurfaceAngle(M_PI/4); // 45 degrees11 gp3.setMinimumAngle(M_PI/18); // 10 degrees12 gp3.setMaximumAngle(2*M_PI/3); // 120 degrees13 gp3.setNormalConsistency(false);


Code2

1 // Create search tree*2 pcl::search::KdTree<pcl::PointNormal>::Ptr tree2 (

new pcl::search::KdTree<pcl::PointNormal>);3 tree2->setInputCloud (cloud_with_normals);4 // Define inputs to thetriangulation structure5 gp3.setInputCloud (cloud_with_normals);6 gp3.setSearchMethod (tree2);7 // Copute the mesh8 pcl::PolygonMesh triangles;9 gp3.reconstruct (triangles);


Results


Outline

1. Introduction





6. Conclusion


Smooth

Works by subdivding triangles and fitting a polynomial to thenew points.

1 pcl::surface::VTKSmoother vtkSmoother;2 vtkSmoother.convertToVTK(mesh);3 vtkSmoother.smoothMeshWindowedSinc();4 vtkSmoother.convertToPCL(mesh);


Bunny


Smooth

Only in trunk right now.


Conclusion

I faster plane detection code using CUDA: realtime, severalplanes at once.

I Poisson in trunk (GSOC: Greg Long)I marching cubes in trunk (GSOC: Greg Long)I PCL-TOCS with TOYOTA: code sprint for improving mesh

reconstruction.I more techniques (e.g. Poisson reconstruction, marching

cubes).


surface reconstruction - point cloud libraryoutline 1.introduction 2.plane detection problem...

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