Illumination Volume Rendering Volume Modeling Volume Rendering by nikeborome

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									Volume Rendering


       Volume Modeling
       Volume Rendering




                          20 Apr. 2000
                Volume Modeling & Rendering
• Some data is more naturally modeled as a volume, not a surface
• You could always convert the volume to a surface, but that’s not
  always best
• Volume rendering: render the volume directly




         Ray-traced isosurface                Same data, rendered
               f(x,y,z)=c                        as a volume
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          Why Bother with Volume Rendering?

• Isn’t surface modeling & rendering easier?

• Show all your data
     – more informative
     – less misleading (the isosurface of noisy data is unpredictable)
• Constructive Solid Geometry (CSG) is natural
• Simpler and more efficient than converting a very complex data
  volume (like the inside of someone’s head) to polygons and then
  rendering them




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                                 Contrasts
        Surface Rendering                            Volume Rendering

• Surface rendering is the "usual"             • Data consists of one or more
  type of rendering.                             (supposedly continuous) fields in
• Data is converted to geometrical               3D.
  primitives (e.g. triangles), which are       • A Transfer Function maps the data
  then drawn.                                    into a volume of RGBA values.
• Everything you see is a 2D surface,          • This volume is rendered directly,
  embedded in a 3D space.                        like a blob of colored jello.
• The conversion to geometrical                • Data is seen more directly; less
  primitives may lose or disguise                likely to be hidden.
  some data.                                   • Works well for complex surfaces.
• Good for opaque objects, objects
  with smooth surface.




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                               Applications
• medical
     – Computed Tomography (CT)
     – Magnetic Resonance Imaging (MRI)
     – Ultrasound


• engineering & science
     – Computational Fluid Dynamics (CFD) – aerodynamic simulations
     – meteorology – weather prediction
     – astrophysics – simulate galaxies


• Computer Graphics
     – Participating media
     – Texels




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          Brief History of Volume Visualization

• 1970’s     modeling & rendering with 3-D grids and octrees
• 1984       ray casting volume models
• 1986       3-D scan conversion of lines, polygons into 3-D grid
• 1987       marching cubes algorithm (convert volume model to
  surface model)
• 1988       direct volume rendering with painter’s algorithm
• 1989       splatting
• 1990’s     volume rendering hardware




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                    Volume Rendering Pipeline
 • Data volumes come in all types: tissue density (CT), relaxation time of
   certain molecules (MRI), windspeed, pressure, temperature, value of
   implicit function.
 • Data volumes are used as input to a transfer function, which produces
   a sample volume of colors and opacities as output.
     – Typical might be a 256x256x64 CT scan
 • That volume is rendered to produce a final image.




                 transfer          sample
  data                                         rendering            final
                 function          volume
volumes                                                            image

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                           Transfer Functions

• The transfer function takes (multiple) scalar data values as input, and
  outputs RGBA

• It gets applied to every voxel in the volume “model”

• It can be very simple (a color lookup table) or very complicated
  (implementing CSG, voxel texturing, etc.)




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                                     Rendering
• Usually one just integrates color through the volume (ray casting)
• Recursive ray tracing is also possible
     – But it gets confusing pretty quickly (shadows, filtered light, reflections, etc)
• For lighting we need surfaces!
     – We can use the magnitude of the local gradient to check for surfaces (for example,
       bone is denser than fat on CT scans)
     – And we can use the (negative of the) gradient direction as a lighting normal!
     – Some, all, or none of the voxels will have surface lighting.

 •And we need material properties!
      –Either assume all the data is one material type,
      –Or use a separate set of segmentation data to
      identify voxel materials.




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                                Some Details
• Regular x-y-z data grids are easiest and fastest to handle, but
  algorithms exist for handling irregular grids like finite element
  models, where the voxels (volume elements) are not all parallelepipeds.
     – Resample it
     – or just deal with it
     – Finite element data, ultrasound data
• Geometrical primitives can be handled by "rasterizing" them into
  data grids.




                                                   This model was rasterized
                                                   and rendered with VolVis


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                           Accumulating Opacity
• By convention, opacity (alpha)
  ranges from 0.0 to 1.0, 1.0 being
  completely opaque.
• Multiple layers of material are
  composited according to their
  opacity.
• An ideal, continuous material takes
  the limit of this process as it goes to
  an infinite number of infinitely thin
  layers (exponentials).

• The local gradient of opacity can be
  used to detect surfaces, and as the
  normal for the lighting equation.




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                           Ray Casting Volumes
• Just integrate color and opacity along the ray
• Simplest scheme just takes equal steps along ray, sampling opacity
  and color
• Grids make it easiest to find the next cell
• It’s simple to include volumes as primitives in a ray tracer
     – clouds, fog, smoke, fire done this way




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                           Trilinear Interpolation
•   How do you compute RGBA values which are not at sample points?
•   Nearest neighbor (point sampling) yields blocky images
•   Trilinear interpolation is better, but slower
•   Just like texture mapping
     – You can even mipmap in 3D




           Nearest Neighbor               Trilinear Interpolation
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                                 Splatting
• Wonderfully simple
• Working back-to-front (or front-to-back), draw a “splat” for each
  chunk of data
• Easy to implement, but not as accurate as ray casting
• works reasonably for non-gridded data




            closeup of a splat

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                            Other Techniques
• Shear-Warp (Lacroute and Levoy)
     – requires a grid
     – sort of like Bresenham for volumes
     – very fast with no hardware acceleration, but implementation is tricky


• Polygons + 3D texture
     – Build a 3D texture, including opacity
     – Draw a stack of polygons from back to front, with that texture
     – Very efficient on machines with hardware acceleration that supports opacity



                                                               3D RGBA
                                                                Texture


      Viewpoint
                                Draw polygons back to front
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                           CSG is Easy
• The transfer function can be used to mask a volume or merge volumes
• You are still confined to the grid, of course

                            not


                  and
 head                      or



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                           Another CSG example




                                             (VolVis again)



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                      Acceleration Techniques
•   Limit yourself to what you can do in cache...
•   …and do multiple blocks if necessary
•   Octrees
•   Quit integration early- that last bit is slowest
•   Error measures
•   Parallelism




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                           Pictures




                                      colliding galaxies




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