图像拼接维维.ppt

基础知识
1 图形学的研究内容
2 3D data types
3 2½-D Data
4 Reconstruction
5 Range Acquisition Methods
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Main Themes
Imaging
Representing 2D images
Modeling
Representing 3D objects
Rendering
Constructing 2D images from 3D models
Animation
Simulating changes over time
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Modeling--Design
Graphics for Engineering and Architectural System
AutoCAD 2002
Interior Design
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Modeling--Reconstruction
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3D Data Types
Point Data
“Point clouds”
Advantage: simplest data type
Disadvantage: no information on adjacency / connectivity
Volumetric Data
Regularly-spaced grid in (x,y,z): “voxels”
For each grid cell, store
Occupancy (binary: occupied / empty)
Density
Other properties
Popular in medical imaging
CAT scans
MRI
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3D Data Types
Advantages:
Can “see inside” an object
Uniform sampling: simpler algorithms
Disadvantages:
Lots of data
Wastes space if only storing a surface
Most “vision” sensors / algorithms returnpoint or surface data
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3D Data Types
Surface Data
Polyhedral
Piecewise planar
Polygons connected together
Most popular: “triangle meshes”
Smooth
Higher-order (quadratic, cubic, etc.) curves
Bézier patches, splines, NURBS, subdivision surfaces, etc.
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3D Data Types
Advantages:
Usually corresponds to what we see
Usually returned by vision sensors / algorithms
Disadvantages:
How to find “surface” for translucent objects?
Parameterization often non-uniform
Non-topology-preserving algorithms difficult
Implicit surfaces (cf. parametric)
Zero set of a 3D function
Usually regularly sampled (voxel grid)
Advantage: easy to write algorithms that change topology
Disadvantage: wasted space, time
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2½-D Data
Image
stores an intensity / color alongeach of a set of regularly-spaced rays in space
Range image
stores a depth along each of a set of regularly-spaced rays in space
Not plete 3D description
does not store objects occluded (from some viewpoint)
View-dependent scene description
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