3D object capture

1
3D object capture

• Capture N views (parts of the object)
• get points on surface of object
• create mesh (infer connectivity) Hugues Hoppe
• filter data (optional)
• Align views bring different pieces in common
  coordinate frame
• align each view with master view OR
• iterate pairwise alignment OR
• all - to - all alignment
• Stitch pieces together
• lterodegt overlapping surfaces, then sew together
  ( Greg Turk ) OR
• construct volumetric representation, then extract
  surface (Brian Curless Kari Pulli)

2
Point acquisition

• Manual
• magnetic devices
• articulated arm
• Automatic
• passive scanning 2 cameras - hard to do point
  correspondence
• active scanning camera projective device
• laser (Cyberware) sheet of light moving
  relative to object
• LCD projectortag each vertical line gt
  need log(Xres) projective images

R,T
3
View alignment

• Pairwise. Basic Tool iterated closest point
• for every vertex of each mesh, find closest point
  on other mesh
• discard pairs that have at least one point on a
  boundary
• compute rigid body transformation (R, T) for one
  of the meshes to minimize sum of squares for all
  distances (gthas closed form solution)
• perform movement repeat until distance below
  threshold
• All to all
• consider physical model of N bodies where 2
  interact if the corresponding meshes have
  overlapping regions.
• compute equilibrium state

4
Mesh Zippering

• Combine surfaces
• delete overlapping portions (use measurement
  confidence to decide from which mesh)
• triangulate space between meshes
• if surfaces not close enough gt extend mesh
  boundary with perpendicular wall

5
Volumetric methods

• Equate each view with volume information
• stuff in front of surface (between surface and
  camera / projector) gt certainly empty
• stuff behind surface gt probably full
• combine volume info from different views extract
  full/empty boundary
• More precise scalar function over space
  distance to closest point on object surface
• negative outside positive inside
• combine perfect data gt take min(abs(di))
• real data for each sample point, for ltclose
  enoughgt meshes, compute weighted average (based
  on confidence)
• extract 0-level isosurface, using Marching Cubes
• Advantages
• water-tight objects - can have surfaces not
  seen by sensor, but inferred from empty/full info

6

• Marching Cubes
• scalar samples on uniform grid
• for each little cube, creates a surface is sample
  signs differ at cube corners
• fast implementation with table lookup
• Volume representation
• signed distance gt smoother surface. Store only
  near object surface.
• hierarchical volume rep (oct-trees) gt better
  memory usage dont have to guess grid
  resolution