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Progressive Encoding and Compression of Surfaces Generated from Point Cloud Data
- J. Smith, G. Petrova, S. Schaefer
Motivation
Digital Michelangelo Project
Progressive Encoding and Compression of Surfaces Generated from - - PowerPoint PPT Presentation
Progressive Encoding and Compression of Surfaces Generated from - - PowerPoint PPT Presentation
Progressive Encoding and Compression of Surfaces Generated from Point Cloud Data J. Smith, G. Petrova, S. Schaefer Texas A&M University Motivation Digital Michelangelo Project Motivation StreetMapper 360 Motivation EarthScope LiDAR
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Motivation
StreetMapper 360
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Motivation
EarthScope LiDAR
SLIDE 5
Motivation
Lunarscience.nasa.gov LiDAR “ILRIS-3D”
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Surface Reconstruction
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Related Work
- Octree Quantification
– [Scnabel and Klein 2006] – [Huang et al. 2006] – [Huang et al. 2008]
- Oriented Normals
– [Deering 1995]
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Related Work
- Compression of wavelet coefficients using a
zero tree encoder
– Laney et al. [2002]
- Compression of a multiscale surflet
representation
– [Chandrasekaran et al. 2009]
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Related Work
- Unstructured polygon meshes
– Too many to mention.
- Compression of structured mesh
– [Saupe and Kuska 2002] – [Lee et al. 2003] – [Lewiner et al. 2004]
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Surface Compression
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Surface Compression
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Related Work
- Construct an octree estimating local regions of
surface with planes for each level of the
- ctree.
– Encode children planes as distances from parent planes [Park and Lee 2009].
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Contributions
- Compression technique for planes estimating
local regions of point clouds
- 2 phase compression
– Pruning of an adaptive octree for removing redundant geometric data – Plane data progressively encoded as displacements
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Point Cloud
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Intermediate Representation
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Generate Implicit
[Manson et al. 2011]
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Generate Surface
[Schaefer and Warren 2004]
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Generate the Octree
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Generate the Octree
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Generate the Octree
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Generate the Octree
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Generate the Octree
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Generate the Octree
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Prune the Octree
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Prune the Octree
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Prune the Octree
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Prune the Octree
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Prune the Octree
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Problems with Pruning
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Extrapolation
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Extrapolation
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Extrapolation
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Extrapolation
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Extrapolation
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Prevent Extrapolation
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Merging
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Merging
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Merging
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Prevent Merging
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Prevent Merging
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Results of Pruning
1179.18 KB 100% 282.47KB 20%
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Encoding Phase
- Progressively encode planes from the root
– Adaptive octree
- Leaf bit
- Children connectivity
– Data per node
- Plane displacements
- Sign bits
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Arithmetic Encoder
- Adaptive Arithmetic Coding [F. Wheeler 1996]
– Source code at http://www.cipr.rpi.edu/˜wheeler/ac
8 bits 10 bits 3 bits
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Connectivity
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Connectivity
1 1 1
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Connectivity
1 1
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Connectivity
1
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Encode Displacement
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Encode Displacement
[Park and Lee 2009]
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Encode Displacement
[Park and Lee 2009]
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Encode Displacement
[Park and Lee 2009]
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Encode Displacement
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Encode Displacement
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Encode Displacement
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Encode Displacement
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Encode Displacement
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Plane Solution
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Plane Solution
i i
d c p n
1
d p 1 n d
1
p
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Problem of Quantization
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Problem of Quantization
2 2
) 1 ( min n
n
subject to
i i
d c p n
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Results
247,064 Polygons
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Results
1,990,811 Polygons
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Results
2,283,540 Polygons
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Comparison
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Comparison
Ours [Park and Lee 2009]
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Limitations
- No guarantee of topology or geometry of
- riginal model.
- Progressive nature does not allow for random
access to arbitrary data in the model
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Conclusion
- Our algorithm is fast
- Outperforms other state of the art methods
2,685,874 Polygons