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Marching Cubes A High Resolution 3D Surface Construction Algorithm.

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Presentation on theme: "Marching Cubes A High Resolution 3D Surface Construction Algorithm."— Presentation transcript:

1 Marching Cubes A High Resolution 3D Surface Construction Algorithm

2 Slice Data to Volumetric Data(1)

3 Slice Data to Volumetric Data(2)

4 Marching Cube Create cells (cubes) Classify each vertex Build an index Get edge list Based on table look-up Interpolate triangle vertices Obtain polygon list and do shading in image space

5 Cube Consider a cube defined by 8 data values, 4 from slice k, and another 4 from slice k+1

6 Classify each vertex Label 1 or 0 as to whether it lies inside or outside the surface Match!!! 0 1 0 1 1 0 0 0

7 Build an index Create an index of 8 bits from the binary labeling of each vertex.

8 Get edge list Give an index, store a list of edges. Because symmetry : 256/2=128 rotation : 128/8=16 256 cases are reduced to 14 cases.

9 Interpolate triangle vertices X = i + ii+1X i X = 20 = 10 iso_value=18iso_value=14 (iso_value - D(i)) (D(i+1) - D(i))

10 Problems about MC Empty cells 30-70% of isosurface generation time was spent in examining empty cells. Speed Ambiguity

11 The Asymptotic Decider Resolving the Ambiguity in Marching Cubes

12 Ambiguity Problem (1) Ambiguous Face : a face that has two diagonally oppsed points with the same sign + +

13 Ambiguity Problem (2) Certain Marching Cubes cases have more than one possible triangulation. Case 6 Case 3 Mismatch!!! + + + + Hole!

14 Ambiguity Problem (3) To fix it … Case 6 Case 3 B Match!!! + + + + The goal is to come up with a consistent triangulation

15 Asymptotic Decider (1) Based on bilinear interpolation over faces B01 B00B10 B11 (s,t) B(s,t) = (1-s, s) B00 B01 B10 B11 1-t t The contour curves of B: {(s,t) | B(s,t) =  } are hyperbolas = B00(1- s)(1- t) + B10(s)(1- t) + B01(1- s)(t) + B11(s)(t)

16 Asymptotic Decider (2) (0,0) (1,1) Asymptote (S  T  If B(S  T  >=  (S  T  Not Separated

17 Asymptotic Decider (3) (1,1) Asymptote (S  T  (0,0) If B(S  T  <  (S  T  Separated

18 Asymptotic Decider (4) (S 1, 1) (S  T  (S 0, 0) S  B00 - B01 B00 + B11 – B01 – B10 T  B00 – B10 B00 + B11 – B01 – B10 B(S  T  B00 B11 + B10 B01 B00 + B11 – B01 – B10 (0, T 0 ) (1, T 1 ) B( S  ) = B( S , 1) B( 0, T  ) = B( 1, T  )

19 Asymptotic Decider (5) case 3, 6, 12, 10, 7, 13 (These are the cases with at least one ambiguious faces)


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