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CSCE 641 Computer Graphics: Image-based Rendering (cont.). Jinxiang Chai. Outline. Light field rendering Plenoptic sampling (light field sampling) Layered depth image/ Post-Rendering 3D Warping View-dependent texture mapping Unstructured lumigraph .

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Csce 641 computer graphics image based rendering cont

CSCE 641 Computer Graphics: Image-based Rendering (cont.)

Jinxiang Chai


Outline
Outline

  • Light field rendering

  • Plenoptic sampling (light field sampling)

  • Layered depth image/Post-Rendering 3D Warping

  • View-dependent texture mapping

  • Unstructured lumigraph


Layered depth image shade et al siggraph98
Layered depth image [Shade et al, SIGGRAPH98]

Layered depth image:

- image with depths


Layered depth image shade et al siggraph981
Layered depth image [Shade et al, SIGGRAPH98]

Layered depth image:

- rays with colors and depths


Layered depth image shade et al siggraph982
Layered depth image [Shade et al, SIGGRAPH98]

Layered depth image: (r,g,b,depth)

- image with depths

- rays with colors and depths


Layered depth image shade et al siggraph983
Layered depth image [Shade et al, SIGGRAPH98]

Rendering from layered depth image


Layered depth image shade et al siggraph984
Layered depth image [Shade et al, SIGGRAPH98]

Rendering from layered depth image

  • - Incremental in X and Y

  • - Forward warping one pixel with depth


Layered depth image shade et al siggraph985
Layered depth image [Shade et al, SIGGRAPH98]

Rendering from layered depth image

  • - Incremental in X and Y

  • - Forward warping one pixel with depth


Layered depth image shade et al siggraph986
Layered depth image [Shade et al, SIGGRAPH98]

Rendering from layered depth image

How to deal with occlusion/visibility problem?

  • - Incremental in X and Y

  • - Forward warping one pixel with depth


How to form ldis
How to form LDIs

  • Synthetic world with known geometry and texture

  • - from multiple depth images

  • - modified ray tracer

  • Real images

  • - reconstruct geometry from multiple images (e.g.,

  • voxel coloring, stereo reconstruction)

  • - form LDIs using multiple images and

  • reconstructed geometry

  • Kinect sensors

  • - record both image data and depth data


Image based rendering using kinect sensors
Image-based Rendering Using Kinect Sensors

  • Capture both video/depth data using kinect sensors

  • Using 3D warping to render a video from a novel view point [e.g., Post-Rendering 3D Warping]

  • Demo: click here


Outline1
Outline

  • Light field rendering

  • Plenoptic sampling (light field sampling)

  • Layered depth image/Post-Rendering 3D Warping

  • View-dependent texture mapping

  • Unstructured lumigraph


View dependent surface representation
View-dependent surface representation

From multiple input image

- reconstruct the geometry

- view-dependent texture


View dependent surface representation1
View-dependent surface representation

From multiple input image

- reconstruct the geometry

- view-dependent texture


View dependent surface representation2
View-dependent surface representation

From multiple input image

- reconstruct the geometry

- view-dependent texture


View dependent surface representation3
View-dependent surface representation

From multiple input image

- reconstruct the geometry

- view-dependent texture


View dependent texture mapping
View-dependent texture mapping

[Debevec et al 98]


View dependent texture mapping1
View-dependent texture mapping

  • - Virtual camera at point D

  • Textures from camera Ci mapped onto triangle faces

  • Blending weights in vertex V

  • Angle θi is used to compute the weight values:

  • wi = exp(-θi2/2σ2)

Subject's 3D proxy points

V

q

q

0

3

q

q

1

2

C

C

C

C

0

1

2

3

D



Outline2
Outline

  • Light field rendering

  • Plenoptic sampling (light field sampling)

  • Layered depth image/Post-Rendering 3D Warping

  • View-dependent texture mapping

  • Unstructured lumigraph


The image based rendering problem
The Image-Based Rendering Problem

  • Synthesize novel views from reference images

    • Static scenes, fixed lighting

    • Flexible geometry and camera configurations


The ulr algorithm siggraph01

LF

VDTM

The ULR Algorithm [Siggraph01]

  • Designed to work over a range of image and geometry configurations

# of Images

Geometric Fidelity


The ulr algorithm siggraph011

LF

VDTM

The ULR Algorithm [Siggraph01]

  • Designed to work over a range of image and geometry configurations

# of Images

ULR

Geometric Fidelity


The ulr algorithm siggraph012

LF

VDTM

The ULR Algorithm [Siggraph01]

  • Designed to work over a range of image and geometry configurations

  • Designed to satisfy desirable properties

# of Images

ULR

Geometric Fidelity


u

u0

s0

s

Desired Camera

“Light Field Rendering,” SIGGRAPH ‘96

Desired color interpolated from

“nearest cameras”


u

s

Desired Camera

“Light Field Rendering,” SIGGRAPH ‘96


“The Scene”

u

Potential

Artifact

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96


“The Scene”

Desired Property #2: Use of geometric proxy

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96


“The Scene”

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96


“The Scene”

Desired Property #3: Unstructured input images

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96

Rebinning

Note: all images are resampled.


“The Scene”

Desired Property #4: Real-time implementation

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96


“The Scene”

Occluded

Out of view

Desired Camera

View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98


“The Scene”

Desired Property #5: Continuous reconstruction

Desired Camera

View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98


“The Scene”

Desired Camera

View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98

θ1

θ3

θ2


“The Scene”

Desired Property #6: Angles measured w.r.t. proxy

Desired Camera

View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98

θ1

θ3

θ2


Desired Camera

“The Scene”


“The Scene”

Desired Property #7: Resolution sensitivity

Desired Camera



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