Image-Based Rendering
David Luebke
University of Virginia
But first…

A funky cactus
Image-Based Rendering

You’ve been learning how to turn
geometric models into images
– Specifically, images of compelling 3D objects
and worlds

Image-based rendering: a relatively new
field of computer graphics devoted to
making images from images

Ex: Quicktime VR
Images with depth

Quicktime VR is really just a 2D
panoramic photograph
– Spin around, zoom in and out

But what if we could assign depth to
parts of the image?

Ex: Tour Into the Picture
Tour Into the Picture

Software for:
– Selecting parts of an image
– Assigning a vanishing point for depth of
background objects
– Assigning depth to foreground objects
– “Painting in” behind objects
Depth per pixel

What if we could assign an exact depth
to every pixel?

Ex: MIT Image-Based Editing system
Depth per pixel continued

What if we had a “camera” that
automatically acquired depth at every
pixel?

Ex: deltasphere
Ex: Monticello project

General image-based
rendering

Can we do anything if we don’t have
depth at every pixel?
– Intuitively, if we had enough images we
should be able to reconstruct new images
from novel viewpoints
– Even without depth information
– This is the problem of pure image-based
rendering
A 4-D Light Field
Creating Light Fields
Light Field as an
Array of Images
Fast Rendering of Light
Fields
Use Gouraud Shading
Hardware!
Light Field Rendering

Demo of Stanford viewer and light
fields…
View-Dependent
Rendering


Spectrum of rendering techniques from
pure IBR to pure geometry
Points in this space:
– Pure IBR: light field/lumigraph
– Depth-per-pixel approaches

Another point: view-based rendering
– Slides at:
http://graphics.stanford.edu/~kapu/vbr/webslides/index.h
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Image-Based Rendering David Luebke University of Virginia