3D Scanner

1. 3D Scanner Julian Timpner | Axel Schön | Matthias Meine | MeikGowin | Sergey Kovalev Team Project • Computer Graphics Lab, TU Braunschweig

2. Tasks • Camera Calibration • Create 3D Object • Mesh Simplification

3. Workflow

4. Image Acquisition • 60 – 80 pictures • Scenery background • Provide enough keypoints • 360° view

5. Pictures/Silhouettes • Create the silhouette manually

6. Camera Calibration • Bundler • State-of-the-art software for camera calibration • Used in Photo Tourism • Input: Set of unordered images and feature detector • Structure-from-motion • Output: Camera Calibration Data, Point clouds (PLY files)

7. Bundler Output • Cameras/Points <num_cameras> <num_points> [two integers] <camera1> ... <cameraN> <point1> ... <pointM> • <camera> <f> <k1> <k2> [the focal length, followed by two radial distortion coeffs] <R> [a 3x3 matrix representing the camera rotation] <t> [a 3-vector describing the camera translation]

8. Matrix Conversion Output from Bundler Convert projection matrices Input for hull reconstruction

9. Matrix Conversion Get projection matrix B. (R00 R01 R02 t0) B = (R10 R11 R12 t1) (R20 R21 R22 t2) ( 0 0 0 1 ) Create intrinsic matrix C. For some reason, the x-component has to be flipped in image space (by negating the focal length). (-f*scale 0 w/2 0 ) C = (0 f*scale h/2 0 ) (0 0 1 0 ) (0 0 0 -0.001) Projection matrix A for undistorted images. A = C * B Create final 3x4 projection matrix P from A's entries aij. (a00/a33 a01/a33 a02/a33 a03/a33) P = (a10/a33 a11/a33 a12/a33 a13/a33) (a20/a33 a21/a33 a22/a33 a23/a33)

10. 3D Reconstruction • Visual Hull Approach • Use camera positions and mask contours to generate viewing cones • Intersect those cones • Intersection points define visual hull

11. 3D Reconstruction • Our choice: Exact Polyhedral Visual Hulls • Better approximation • Decreased complexity • Output: mesh in .off file format

12. Off2Obj Converter • Interface to the “Virtual Navigation” team .off mesh from EPVH Convert .off into .obj .obj mesh

13. Object • Example output

14. Advantages • Only one camera needed • No manual camera calibration • Fully automatic, only silhouette images needed

15. Problems • Silhouette Carving • Used algorithm crashed while working with our input: our matrices seemed to be incompatible with it • Results worse than EPVH • Marching Cubes • The voxels extracted from the Silhouette Carving example could not be connected properly for creating a mesh

16. SC/MC Results

17. Problems • Bundler Point Cloud • Too much noise, mostly too few object points

18. Open issue: Textures • Texture Mapping • Projective Textures

19. Textures Texture Mapping • Put 3D object on 2D texture • Texture Mapping algorithm maps 3D to 2D coordinates

20. Textures Projective Textures • Put 2D texture on 3D object

21. Thank you for your attention! Any questions?