saito, t. and takahashi, t. comprehensible rendering of 3-d shapes proc. of siggraph '90...
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Saito, T. and Takahashi, T.Comprehensible Rendering of 3-D ShapesProc. of SIGGRAPH '90
Genesis of Genesis of Image Space NPRImage Space NPR
• Operations on G-buffers to extract certain properties various images
• Combine these images with rendered images
Image space algorithms Saito, T. and Takahashi, T.Comprehensible Rendering of 3-D ShapesProc. of SIGGRAPH '90
G-buffers ?
Computer-Generated Images
Special kind of recording equipment yields special images
• x-ray images• thermal images• sonar images
G-buffers
• Translate this approach to computer graphics
• Render algorithms to create images that show scene properties normally hidden to the
• viewer
• object ID
• distance to view plane
• surface normal
• patch coordinates (u,v) for spline surfaces
• …
• G-buffers (geometric buffers)
• PixelPixel color now encodes 3D information color now encodes 3D information and not just illuminationand not just illumination
• RReveal information about the underlyingeveal information about the underlying geometrygeometry
• OOperations on G-buffersperations on G-buffers• combinationcombination• edge detectionedge detection• ……
G-buffers
evealRGB-buffer
evealObject ID-buffer
evealDepth-buffer
evealNormal-buffer
evealRGB-buffer
evealObject ID-buffer
evealDepth-buffer
evealNormal-buffer
Process pixel (x,y)
Saito, T. and Takahashi, T.Comprehensible Rendering of 3-D ShapesProc. of SIGGRAPH '90
Data structures + algorithms:
• Drawing discontinuities, edges, contour lines, curved hatching from the image buffer
Edge classification :
• Profile - - the border line of an object on the screen• Internal - - a line where two faces meet.
Images generated:
1. Depth2. First-order differential3. Second-order differential4. Profile5. Internal edge
vs wz
dz
2
Depth Image
Distance: viewpoint to screen Depth of object(eye coordinate)
One pixel length(eye coordinate)
Grayscale image that maps [dmin, dmax] to [0, 255]
Shaded image
Depth image
OpenGL depth image content extracted by glReadPixels with GL_DEPTH_COMPONENT
Equalizes the gradient value of depth image with the slope of the surface.
Depth ImageGrayscale image that maps [dmin, dmax] to [0, 255]
HGF
EXD
CBA
8
22
22
RDAHEC
HGFCBA
Xg
3
8 HGFEDCBAXXl
Depth image
First-order differential
Sobel’s filter
Second-order differential
p
gk
gg
gg
gg
min
minmax
min gkggif minmax
gkggif minmax
e
lkg
l
l
2max
lkggif minmax
lkgif max
Profile image
Internal edge image
Normalization of images
g
2lk
10gk
l
Distinguishes discontinuities from continuous changes
Limit of the gradient for the elimination of 0th order discontinuities
OID (Object ID) Image
Operations on G-buffers (so far…) Edge detection
• RGB-buffer discontinuities in brightness (illumination), i.e., shadows, material, objects
• z-buffer discontinuities in depth, i.e. Object boundaries, also boundaries within one object (creases)
• OID-buffer discontinuities in “objects”, i.e., object silhouettes
Schofield, S..Non-photorealistic Rendering: A critical examination and proposed systemPhD thesis, School of Art and Design, Middlesex University, May 1994
http://www.microgds.com/index.shtml
We can augment the silhouette edgescomputed with the depth map by usingsurface normals as well.
We will do this by using a normal map,which is an image that represents the surface normal at each point on anobject.
The values in each of the (R; G;B) colorcomponents of a point on thenormal map correspond to the (x; y; z)surface normal at that point.
Depth map
Normal map
Decaudin, P.Cartoon-looking rendering of 3d-scenes.Research Report #2919, INRIA Rocquencourt 1996.
Using Normal Maps to Find Creases and Boundaries
To compute the normal map for an object with a graphics package:
• First, we set the object color to white, and the material property to diffuse reflection.
• We then place a red light on the X axis, a green light on the Y axis, and a blue light on the Z axis, all facing the object. Additionally, we put lights with negative intensity on the opposite side of each axis.
• We then render the scene to produce the normal map. Each light will illuminate a point on the object in proportion to the dot product of the surface normal with the
light’s axis. An example is shown in Figure (c,d).
• We can then detect edges in the normal map. These edges detect changes in surface orientation, and can be combined with the edges of the depth map to produce a reasonably good silhouette image (Figure (e)).
Outline drawing with image processing. (a) Depth map. (b) Edges of the depth map. (c)Normal map. (d) Edges of the normal map. (e) The combined edge images.
Outline detection of a more complex model. (a) Depth map. (b) Depth map edges. (c)Normal map. (d) Normal map edges. (e) Combined depth and normal map edges.
• Creates visible silhouette edges with constant thickness at the same depth value as the corresponding polygon edge
• Works well when dihedral angle between the adjacent front- and back-facing is not large
• As the line width increase, gaps may occur between silhouette edges
Rossignac, J. and van Emmerik, M.Hidden contours on a frame-bufferProc. of the 7th Eurographics Workshop on Computer Graphics Hardware, 1992.
1. Fill background with white
2. Enable back-face culling, set depth function to “Less Than””
3. Render front-face polygons in white
4. Enable front-face culling, set depth function to “Less Than or Equal To”
5. In black, draw back-facing polygons in wire-frame mode.
6. Repeat for a new viewpoint
Rossignac, J. and van Emmerik, M.Hidden contours on a frame-bufferProc. of the 7th Eurographics Workshop on Computer Graphics Hardware, 1992.
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