Presentation in pdf format - University of Minnesota Duluth

Siddharth Deokar
Graduate Research Student
Department of Computer Science
University of Minnesota Duluth
Advised by
Pete Willemsen
May 22, 2009

Outline
Problem Overview
Background
System Overview
Sorting on the GPU & Blending
Aggregate Snow
LDR, HDR Rendering
Modeling of snow
Results
Conclusion
2

Problem Overview (The Big
Picture)
Snowing conditions
Real Life Problem
Following behind a snowplow or other vehicles can be dangerous
Difficult to see other vehicles or judge their speed
Due to poor visibility and snow, it becomes difficult to detect
speed changes of other vehicles resulting in the potential for rear
end collisions
Why snow rendering ?
Create a virtual interactive environment where a person can
drive around in blowing snow and foggy conditions.
Understand how we drive in snowing conditions
Try to make winter driving safe
3

Problem Overview Continued…
Goal of my research
Effective rendering of snowing conditions
Improve visual rendering of snow
Interaction between light and snow
Create more realistic scenes
Experiment Framework: Simulated Highway [Pete
Willemsen] & Snow Plow [Michele Olsen]
Video (SnowRenderingSimulation.mov)
4

Background
GPU
Why GPU ?
Many Stream Processors
SIMD Architecture
Massive Parallelization
Dedicated for rendering high end graphics like video games, 3D games
Textures
Main memory structure
of a GPU
Each colored square is an individual
texel, which can be a vector values
Shaders ?
One shader for updating positions
Another Shader for sorting positions
12 13 14 15
8 9 10 11
4 5 6 7
0 1 2 3
5

Background continued…
Particle System [Andrew Norgren]
Point Sprites
Render millions of particles in real time
Video (ParticlesAsPointSprites.mov)
6

System Overview
Sorting & Blending
Why sorting or blending ?
Quick Sort
GPU Sort
Additive Blend
Aggregate Snow Effect
High Dynamic Range Rendering
Model snow particles
Transparency
Scattering of Light
7

Sorting & Blending on the GPU
Why is sorting required ?
Objects are drawn in a sequence which is provided by
the user in his code
Problem with
transparent
objects blocking
objects behind
them
Video (unsorted
particles.mov)
8

Sorting of Snow Particles
Sort the particles based on each individual particle’s
distance from the eye
Euclidean Distance
Quick Sort
Sorting done by CPU
Maps with GPU memory every frame to sort
Map the particle positions from the vertex buffer to CPU
memory
9

Sorting of Snow Particles
Continued…
GPU Sort
Inherent Parallelism
Gets rid of CPU communication
Faster than using the best sorting algorithms on the
CPU
Algorithms like Quick Sort cannot be implemented on the
GPU as it cannot write to arbitrary memory locations
The GPU does not support this functionality to avoid write
after read operations by different stream processors when
accessing the same memory location
10

GPU Sort
Implemented by Govindaraju, N., Raghuvanshi, N.,
Henson, M., Tuft, D., Manocha, D. [A Cache Efficient
Sorting Algorithm for Database and Data Mining
Computations using Graphics Processors, 2005]
They have used texture mapping to implement a
bitonic sort algorithm
I have modified the algorithm to suit the sorting of
particles in our system
11

GPU Sort Continued…
Bitonic Sequence
A sequence which has at most one local minimum or
maximum
When you break a bitonic sequence at the minimum or
maximum you get two sorted sequences, one ascending and
the other descending
Examples: 1,2,3,4,5,6,7,8 2,5,8,10,7,4,3,1
9,8,5,3,6,10,11,12
splitting the second sequence at 10 gives us 2,5,8,10 and 7,4,3,1 which
are sequences in ascending and descending order respectively
In the third example, the minimum is 3 and splitting the sequence at
3 gives us 9, 8, 5, 3 and 6, 10, 11, 12 which are sequences in descending
and ascending order respectively
12

GPU Sort Continued…
(Bitonic Sort)
Stage 1: Bitonic Sequence
Created
Stage 2: Bitonic
Sort
Pass 1 Pass 2 Pass 3 Pass 4 Pass 5 Pass 6
13

Example of Bitonic Sort
Unsorted Sequence 12 8 14 6 15 10 9 13
Pass 1 12 8 14 6 15 10 9 13
Result 8 12 14 6 10 15 13 9
Pass 2 8 12 14 6 10 15 13 9
Result 8 6 14 12 13 15 10 9
Pass 3 8 6 14 12 13 15 10 9
Bitonic Sequence 6 8 12 14 15 13 10 9
Pass 4 6 8 12 14 15 13 10 9
Result 6 8 10 9 15 13 12 14
Pass 5 6 8 10 9 15 13 12 14
Result 6 8 10 9 12 13 15 14
Pass 6 6 8 10 9 12 13 15 14
Sorted Sequence 6 8 9 10 12 13 14 15
14

GPU Sort Continued…
Applying GPU Sort to our system
The particles are stored in a 2-D texture where each cell
holds the position of a particle as a 4-D vector
containing values
Calculate Euclidean distance of each particle from eye
Indices for particles ?
Indices Distances GPU Sort Sorted Indices
1 2 3 4 25 38 33 28 1 14 9 4
5 6 7 8 + 40 32 27 36 = 16 8 3 12
9 10 11 12 37 34 26 31 13 10 2 7
13 14 15 16 29 35 39 30 5 11 15 6
15

GPU Sort Continued…
Drawback
GPU Sort is very fast for a small number of particles but
becomes slow as the number of particles increases
For example, the time taken to render a frame with one
million particles is more than 0.01 seconds which is necessary
for an interactive environment.
Solution
GPU Sort done over a number of passes
It is Fast but,
This approach is restricted as the particles are moving and hence we
cannot have a large number of passes to sort the particles in a
particular position
On the other hand, by using a few passes gives us artifacts which are
easily noticeable to the human eye
16

Additive Blending
Alternative to sorting
Alpha value is used to combine the color value of a
fragment with
the color of a
pixel already
stored in the
framebuffer
17

GPU Sort and Blending Continued…
Video (Sorting.mov)
18

Aggregate Snow Effects
Snow particles are confined in a certain domain
To show snow particles everywhere is not
computationally feasible
In real world it is hard to see individual snow particles
after a certain distance
Aggregation of snow becomes more prominent as you
look into the horizon with objects becoming less and
less visible with increasing distance
19

Aggregate Snow Effects Continued…
Visibility
As the amount of snow increases, visibility decreases
Derive a relation between the snow density in our simulation
system and visibility in the simulated scene
Video (Real World Aggregation.mov)
20

Aggregation: Snow Density
21

Aggregate Snow Effect
Video (Visibility.mov)
22

High Dynamic Range Rendering
Our visual system supports a very high contrast ratio which
is not reflected in graphical scenes
Low Dynamic Range Rendering
8 bits per color (RGB)
Contrast ratio of 256:1 per color
High Dynamic Range Rendering
Better contrast ratio
Create more realistic scenes
Example: Light Source (yellow)
LDR RGB = {1,1,0}
HDR RGB = {4,4,0}
4 times brighter than a light source with intensity {1,1,0}
23

HDR Rendering Process
Render Scene
Extract High
Luminance
Downsample
& Blur
Additive
Blending
Final Image
Bloom Mask
24

HDR Rendering Continued…
Render using
HDR
Extract High Luminance
Downsampling & Blur
Bloom Mask
Image rendered
using HDR
Final Image
25

HDR Rendering Continued…
HDR Scene is tone mapped in order to be displayed on
the monitor
HDR Image Tone Mapping LDR Image
Tone Mapping
26

HDR Rendering Continued…
Final Image displayed on screen
27

Modeling of Snow
Snow
Translucent particles
Interact with light to produce scattering effect
Model shape of a snow particle, appearance and
scattering of light due to snow
28

Transparency
Gaussian Function
Gives snow opacity at the centre and becomes
transparent as we move away from the centre
29

Shapes for snow particles
Particles are modeled after actual snow flakes
These images give an outer boundary to our snow
particles so that they resemble actual snowflakes
30

Shapes for snow particles
continued…
Particles as snow flakes
Gaussian Transparency
Snow flake
Final Image with
Gaussian transparency
and snow flake shape
31

Dynamic Lights
We can have many lights active in the system at any given
moment of time
Light source information is available through use of textures
Data 1 Position 1 Intensity 1 Direction 1 Spotlight
Parameters 1
Data 2 Position 2 Intensity 2 Direction 2 Spotlight
Parameters 2
Data 3 Position 3 Intensity 3 Direction 3 Spotlight
Parameters 3
Data 4 Position 4 Intensity 4 Direction 4 Spotlight
Use active lights for scattering calculations
Parameters 4
32

Scattering
Ray Tracing
Tracing path of light through every pixel
Computationally expensive
Phase /Scattering function
Henyey Greenstein Phase Function
Widely used in atmospheric
sciences because of its simplicity
eye
θ
33

Scattering Continued…
Single parameter g (asymmetry parameter) controls
the distribution of scattered light
Forward Scattering
Back Scattering
34

Scattering
Video (scattering.mov)
35

Results
Sorting
4
3.5
3
2.5
2
1.5
Time In 1
seconds
0.5
0
15K 65K 250K 500K 1M 2M
Number of Particles
Quick Sort
GPU Sort with
Passes
GPU Sort w/o
Passes
36

Results Continued…
GPU Sort (With Passes Vs Without Passes)
0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
Time In
seconds
0.004
0.002
0
15K 65K 250K 500K 1M 2M
Number of Particles
GPU Sort with
Passes
GPU Sort w/o
Passes
37

Results Continued…
Visibility
Image 1 = > Visibility 0.76 (Less snow 6000 particles/s)
Image 2 = > Visibility 0.57 (Heavy Snow 600000 particles/s)
Image 3 = > Visibility 0.16 (Very Heavy Snow 900000
particles/s)
38

Results Continued…
Snow Model
OSG Snow (Video – osgSnowRendering.mov)
Our snow model
Snow system with all the effects like Gaussian
transparency, scattering, HDR and fog
39

Summary
1. Sort the particles based on distance from eye OR use additive blend
2. Model snow particle
3. Get the number of lights in the scene
4. Calculate color for each particle
5. Calculate effect of every light on the particle
6. Apply Gaussian Transformation
7. Apply Scattering Function
8. Apply snow aggregation to other objects in the scene
9. Render scene to a frame buffer using HDR for lighting calculations
10. Render HDR image
11. Down sample and apply Gaussian blur
12. Apply resulting bloom mask to original image
13. Render final image
40

Snow Model
Video (snow.mov)
41

Implementation
Implemented on top of particle system created by Andrew
Norgren, Pete Willemsen
Implemented in C++ with OpenGL and OpenGL Shading
Language
Tested on 2.4 GHz AMD Dual Core Processor with a
NVIDIA GeForce 8800 Ultra graphics card
128 Stream Processors
Memory – 728 MB
Matlab for snow density calculations
42

Conclusion
A snow domain which is more realistic to outside world
Individual & Aggregate Snow modeled effectively
Interactive Environment (Real Time !)
Future
Tie visibility with real world data
Sharp snow particles in the near plane and blurry snow
(gaussian) at a distance
Sorting particles near the viewer
Shadowing
43

Acknowledgements
Pete Willemsen
L. Zimmerman
Andrew Norgren, Michele Olsen
44

Thank You
Questions ?