csce 641 computer graphics image based rendering cont
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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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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 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

slide25

u

u0

s0

s

Desired Camera

“Light Field Rendering,” SIGGRAPH ‘96

Desired color interpolated from

“nearest cameras”

slide26

u

s

Desired Camera

“Light Field Rendering,” SIGGRAPH ‘96

slide27

“The Scene”

u

Potential

Artifact

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96

slide28

“The Scene”

Desired Property #2: Use of geometric proxy

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96

slide29

“The Scene”

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96

slide30

“The Scene”

Desired Property #3: Unstructured input images

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96

Rebinning

Note: all images are resampled.

slide31

“The Scene”

Desired Property #4: Real-time implementation

Desired Camera

“The Lumigraph,” SIGGRAPH ‘96

slide32

“The Scene”

Occluded

Out of view

Desired Camera

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

slide33

“The Scene”

Desired Property #5: Continuous reconstruction

Desired Camera

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

slide34

“The Scene”

Desired Camera

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

θ1

θ3

θ2

slide35

“The Scene”

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

Desired Camera

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

θ1

θ3

θ2

slide36

Desired Camera

“The Scene”

slide37

“The Scene”

Desired Property #7: Resolution sensitivity

Desired Camera

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