amcs / cs 247 – scientific visualization lecture 22 ... · reading assignment #12 (until dec. 3)...
TRANSCRIPT
AMCS / CS 247 – Scientific VisualizationLecture 22: Vector Field / Flow Visualization, Pt. 6
Markus Hadwiger, KAUST
2
Reading Assignment #12 (until Dec. 3)
Read (required):• Data Visualization book, Chapters 6.7, 6.8
• J. van Wijk: Image-Based Flow Visualization, ACM SIGGRAPH 2002http://www.win.tue.nl/~vanwijk/ibfv/ibfv.pdf
Read (optional):
• J. van Wijk: Image-Based Flow Visualization for Curved Surfaces,IEEE Visualization 2003http://www.win.tue.nl/~vanwijk/ibfv/ibfvs.pdf
• R. Laramee, B. Jobard, H. Hauser: Image Space Based Visualization of Unsteady Flow on Surfaces, IEEE Visualization 2003http://www.computer.org/portal/web/csdl/doi/10.1109/VISUAL.2003.125036
3
Quiz #3: Dec. 3
Organization• First 30 min of lecture
• No material (book, notes, ...) allowed
Content of questions• Lectures (both actual lectures and slides)
• Reading assignments (except optional ones)
• Programming assignments (algorithms, methods)
• Solve short practical examples
Texture Advection
KAUST King Abdullah University of Science and Technology 4
Texture Advection
• Advect texture / images along flow
• Usually noise textures and dye
• Regularly inject new noise
5
Also for unsteady flow, i.e.,time-dependent vector fields!
Lagrangian vs. Eulerian
• Lagrangian: move along with the particle
• Eulerian: consider fixed point in space, look at particles moving through
• Example for pixels: rotate image (a),Lagrangian: move pixels forward (b),Eulerian: fetch pixels from backward direction (c)
Markus Hadwiger, KAUST 6
Lagrangian-Eulerian Advection (LEA)
• Influence ofthe blend factorbetween freshnoise and theadvected noise
KAUST King Abdullah University of Science and Technology 12
13
Image Based Flow Visualization
J. van Wijk: “Image Based Flow Visualization” in Proceedings of ACM SIGGRAPH 2002
Image Based Flow Visualization
14
Image Based Flow Visualization
15
Algorithm
1. Calculate distorted mesh R (if flow field has changed)
2. Render R, texture mapped with previous image
3. Blend with noise image, using factor α (weight previous with 1 – α)
4. Draw (inject) dye if desired
Image Based Flow Visualization
16
Blending of (low-pass filtered) noise images
Results in convolution with exponential kernel:
Exponential decay of image G
Image Based Flow Visualization
17
Noise image generation
• Use interpolated image of random values: s is scale parameter
• h() is triangular pulse
• Random values on grid to interpolate: rate of change vg,random phase ϕ, periodic function w() (square, saw)
Image Based Flow Visualization
18
Spatial noise frequency(scale parameter s)determines visual style
Like LIC
Like spot noise
Blurred
Image Based Flow Visualization
19
Periodic function w()
Cosine
Square
Exponential decay
Saw tooth
Image Based Flow Visualization
20
21
IBFV on Surfaces
J. van Wijk: “Image Based Flow Visualization for Curved Surfaces” in Proceedings of IEEE Visualization 2003
22
IBFV on Surfaces (IBFVS)
IBFVS pipeline
• Advect noise patterns in directionof vector field
• Done using 3D vector fieldprojected to 2D image space→ flow visualization oncurved surfaces
23
Image Space Flow Vis on Surfaces
R. Laramee, B. Jobard, H. Hauser: “Image Space Based Visualization of Unsteady Flow on Surfaces” in Proceedings of IEEE Visualization 2003
24
Image Space Flow Vis on Surfaces
25
Image Space Flow Vis on Surfaces
Meshes can be time-dependent
Example: intake valve and piston cylinder
26
Example: Visualize Curvature Directions
Vector field is field of principal curvature directions
Thank you.
Thanks for material• Ronny Peikert
• Helwig Hauser
• Meister Eduard Groeller
• Jens Krüger