3D reconstruction using hierarchical surface patches

1. 3D reconstruction using hierarchical surface patches Motivations With the improvements of three-dimensional scanners, data size produced is now so important that only a few percentage of the whole data is needed to fully represent objects. Moreover, scenes in the real life may contain several objects partially defined and with complicated shapes. The problem here is a complex scene representation problem. • Objectives • Reconstruct 3D meshes from real data • Extract objects from a global complex scene • Reduce data size needed for equivalent representation • Handle topological complex objects with holes or boundaries • Allow different levels of representation

2. INDEX • I Problem and Objectives • II Research Directions • III Possible Solutions • IV Accomplishments

3. I Problem and Objectives

4. General problems • How can we reconstruct a mesh to represent a surface from a cloud points? • What do we know about the properties of the cloud point? • What about the reconstructed surface properties? Example : range image from IRIS 3D scan archive Original range image Reconstructed surface

5. Range Images already contain point neighborhood information So, a first mesh is easily created by mapping a regular grid on the point cloud Range Images Produce Structured Mesh This property allows us to define a first approach avoiding 3D points ordering operation !

6. Cloud Points Reconstructed Mesh Result Example with Mannequin head reconstructed cloud point (IRIS LAB Database) What about non ordered cloud points? It’s an other part of the problem : • points no more ordered. • So what kind of techniques are possible to use? • How order the points in 3D? • Possible solutions (?) • Local neighborhood • tangent plane projection • …

7. SURFACE PATCHES Automatic Reconstruction of Surfaces and Scalar Fields from 3D Scans Chandrajit L. Bajaj Fausto Bernardin Guoliang Xu Department of Computer Sciences Purdue University SUPERQUADRICS Images from: Yan Zhang’s superquadrics reconstruction, IRIS LAB University of Tennessee SKELETON EXTRACTION J. C. Carr R. K. Beatson J. B. Cherrie T. J. Mitchell W. R. Fright B. C. McCallum T. R. Evans University of Canterbury Example of Existing Primitive • Primitive representation • Higher abstract level representation • Data size reduction

8. II Research Directions

9. Images from Michael Roy’s works on multiresolution meshes, IRIS LAB University of Tennessee • Many possible directions : • Primitives extraction / features detection  reduce the number of parameters (patches, subdivisions surfaces…) • Hierarchical reconstruction  to be able to represent the surface at many resolution levels • Reconstruction itself  properties, quality, speed…

10. III Possible Solutions

11. Possible Solutions (?) This part is subject to evolve through the time. But we already have many ideas : • Find “good” curves on cloud points in order to create an initial parameterization for rough patches • Define for each coarse patch a rough surface approximating the mesh • Extract higher frequencies (details) from the differences between the real mesh and the approximating patch1 Parameterization among iso-value curves

12. IV Accomplishments

13. Point Cloud Segmentation Set of curves in 3D Parametric domain definition New Point Cloud Approximating Surface Details extraction Simplification General Algorithm In progress

14. Analysis/Synthesis Approximating surface “Details” Surface Original surface = - Original surface Approximation Do you see any difference?

15. Details Details are obtained simply by difference between original and approximation surfaces

16. Future works • Finish the algorithm to handle range images fully • Approximating surface • “Finish” the fitting method • Compare results • Detail Surface • Edge curves extraction to construct a patch (Coon Patch ) • Both : • Simplification / update parameterization • Pasting operation • Extension to 3D point clouds