Image-Based Rendering using Hardware Accelerated Dynamic Textures

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Image-Based Rendering using Hardware Accelerated
Dynamic Textures

• Keith Yerex
• Dana Cobzas Martin Jagersand

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Motivation

• Rendering

• Traditional geometry based techniques
• detailed 3D model texture
• hard to achieve photorealism

• Image-based models
• non-geometric model from images
• practically hard to apply

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Challenges

• Hard to generate detailed 3D models
• Texturing from images require very precise
  alignment with the model
• Rendering arbitrary views using IBM requires
  dense sample of the plenoptic function
• IBR techniques dont deal with dynamic scenes

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Overview
Training
Model
New view
I1
It
Structure P
New pose (R a b)



(R1 a1 b1) (Rt at bt)
Motion params


Texture basis
Warped texture
y1 yt
Texture coeff
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Structure from motion

• Input Structure from set of corresponding
  points tracked in a set of images
• Assumptions
• Static scene
• Camera model
• injective ? perspective ? weak perspective ?
  orthographic
• Estimated model
• projective ? affine ? metric ? euclidean

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Structure from motion
Tracked features
poses
structure
Structure from motion algorithm
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SFM algorithms

• Few images, perspective camera, precise
  calibration
• epipolar constraint
• trilinear tensor
• Long motion, affine or perspective structure
• factorization methods

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Metric structure

• Weak perspective camera
• Extension of Tomasi Kanade factorisation
• algorithm
• Extract affine structure
• Relation between the sffine structure and camera
  coordinate frame
• Transform the structure into metric (unit pixel
  size)

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Weak perspective projection

• N points
• Normalized with respect to centroid
• Rank theorem
• Factorization

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Metric constraints

• Extract motion parameters
• Eliminate scale
• Compute direction of camera axis k i x j
• parameterize rotation with Euler angles
• Model P Reprojection
• Pose x (r,s,a,b)

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Dynamic Textures

• Purpose
• Model image intensity variations due to
• Small geometric errors due to tracking
• Non planarity of real surface
• Non-rigidity of real object
• Pose varying lighting effects
• Non-geometric, mixing of spatial basis

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Spatial Basis Intro

1. Moving sine wave can be modeled
2. Small image motion

Spatially fixed basis
2 basis vectors
6 basis vectors
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Image Variability

• Formally consider residual variation in an image
  stabilization problem
• Optic flow type constraint

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Structural Image Variability

• Affine warp function
• Corresponding image variability
• Discretized for images

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Composite Image variability

• Similarily can show that composite image
  variability
• Can be modeled as sum of basis

Struct Depth Non-plan Light Res Err
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Example Lighting variation
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Statistical Image Variability

• In practice image variability hard to compute
  from one image
• Instead we use PCA to estimate image variability
  from a large sequence of images
• This yields a transformed basis
• Can estimate linear model J
• In practice Delaunay triang bi-linear model

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Image variability comparison

Derivatives from one picture
Statistically estimated variability
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Implementation

• Matlab for geometric modeling and prototyping
• mexVision for tracking (30Hz frame rate)
• Hardware accelerated OpenGL for rendering (2.8Hz
  in SW, 18Hz on GeForce 2)
• pthreads and pvm for parallel processing

MATLAB
OpenGL
meXVision
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Hardware rendering

• Unsigned basis
• Scaling to 8 bit
• Where

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OpenGL
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Example Renderings
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Kinematic arm
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Geometric errors
static
dynamic
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Geometric errors
dynamic
static
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Geometric errors
Dynamic
Static
Dynamic
Static texturing
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Pixel error
Vertical jitter Horizontal jitter
Static texture 1.15 0.98
Dynamic texture 0.52 0.71
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Conclusions

• Coarse geometry tractable to estimate
• Errors from small geometric misalignments
  compensated for using dynamic texture
• System runs on consumer PC with web cam and game
  graphics card
• Applications
• Insert characters/objects into games
• Video phone