Surface Reconstruction. Vincent Rabaud

Transcription

1 Surface Reconstruction Vincent Rabaud November 03, 2011

2 Outline 1. Introduction 2. Plane Detection Problem definition Plane estimation Hull refinement 3. Moving least squares Problem Gist 4. Triangulation Meshing 5. Smoothing VTK smoother Poisson 6. Conclusion

3 Outline 1. Introduction 2. Plane Detection Problem definition Plane estimation Hull refinement 3. Moving least squares Problem Gist 4. Triangulation Meshing 5. Smoothing VTK smoother Poisson 6. Conclusion

4 Introduction Wanted: surface/mesh from a point cloud Why? the world is not sparse for better graphics/visualization for texture mapping CAD models compression

5 Introduction In robotics: object detection object grasping

6 Plane detection We are given a noisy point cloud and we want to find the best plane

7 Plane detection 1 // Define the RANSAC object 2 pcl::sacsegmentation<pcl::pointxyz> seg; 3 // Define its coefficients 4 seg.setoptimizecoefficients (true); 5 seg.setmethodtype (pcl::sac_ransac); 6 // Specify the plane parameters 7 seg.setmodeltype (pcl::sacmodel_plane); 8 seg.setdistancethreshold (0.01); 9 // Specify the input 10 seg.setinputcloud (cloud_filtered); 11 // Segment 12 pcl::modelcoefficients::ptr coefficients (new pcl:: ModelCoefficients); 13 pcl::pointindices::ptr inliers (new pcl:: PointIndices); 14 seg.segment (*inliers, *coefficients);

8 Hull refinement 1 // Create the projection structure 2 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 cloud 7 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 inliers 10 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);

9 Plane detection What a nice convex hull!

10 Outline 1. Introduction 2. Plane Detection Problem definition Plane estimation Hull refinement 3. Moving least squares Problem Gist 4. Triangulation Meshing 5. Smoothing VTK smoother Poisson 6. Conclusion

11 Problem Noisy input cloud, normals are all over the place

12 Moving Least squares Input noisy cloud is named: cloud 1 // Moving least square object 2 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 neighbors 7 pcl::search::kdtree<pcl::pointxyz>::ptr tree 8 (new pcl::search::kdtree<pcl::pointxyz>); 9 tree->setinputcloud (cloud); 10 mls.setsearchmethod (tree);

13 Performing the smoothing 1 // Define the output and the normals 2 pcl::pointcloud<pcl::pointxyz> mls_points; 3 pcl::pointcloud<pcl::normal>::ptr mls_normals (new 4 pcl::pointcloud<pcl::normal> ()); 5 mls.setoutputnormals (mls_normals); 6 // Compute the smoothed cloud 7 mls.reconstruct (mls_points); 8 // Extra: merge fields 9 pcl::pointcloud<pcl::pointnormal> mls_cloud; 10 pcl::concatenatefields (mls_points, *mls_normals, mls_cloud);

14 Result 1

15 Result 2 And even more smoothing

16 Outline 1. Introduction 2. Plane Detection Problem definition Plane estimation Hull refinement 3. Moving least squares Problem Gist 4. Triangulation Meshing 5. Smoothing VTK smoother Poisson 6. Conclusion

17 Meshing method based on growing neighborhoods connect neighbors and increase the neighborhoods untill all points are connected works best for smooth regions

18 Code 1 // Initialize objects 2 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 parameters 8 gp3.setmu(2.5); 9 gp3.setmaximumnearestneighbors(100); 10 gp3.setmaximumsurfaceangle(m_pi/4); // 45 degrees 11 gp3.setminimumangle(m_pi/18); // 10 degrees 12 gp3.setmaximumangle(2*m_pi/3); // 120 degrees 13 gp3.setnormalconsistency(false);

19 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 structure 5 gp3.setinputcloud (cloud_with_normals); 6 gp3.setsearchmethod (tree2); 7 // Copute the mesh 8 pcl::polygonmesh triangles; 9 gp3.reconstruct (triangles);

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21 Outline 1. Introduction 2. Plane Detection Problem definition Plane estimation Hull refinement 3. Moving least squares Problem Gist 4. Triangulation Meshing 5. Smoothing VTK smoother Poisson 6. Conclusion

22 Smooth Works by subdivding triangles and fitting a polynomial to the new points. 1 pcl::surface::vtksmoother vtksmoother; 2 vtksmoother.converttovtk(mesh); 3 vtksmoother.smoothmeshwindowedsinc(); 4 vtksmoother.converttopcl(mesh);

23 Bunny

24 Smooth Only in trunk right now.

25 Conclusion faster plane detection code using CUDA: realtime, several planes at once. Poisson in trunk (GSOC: Greg Long) marching cubes in trunk (GSOC: Greg Long) PCL-TOCS with TOYOTA: code sprint for improving mesh reconstruction. more techniques (e.g. Poisson reconstruction, marching cubes).