spectral compression of mesh geometry (karni and gotsman 2000)
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Spectral Compression of Mesh Geometry (Karni and Gotsman 2000). Presenter: Eric Lorimer. Overview. Background Spectral Compression Evaluation Recent Work Future Directions. Background. Mesh geometry compressed separately from mesh connectivity - PowerPoint PPT PresentationTRANSCRIPT
Spectral Compression of Mesh Geometry
(Karni and Gotsman 2000)
Presenter: Eric Lorimer
Overview• Background• Spectral Compression• Evaluation• Recent Work • Future Directions
Background• Mesh geometry compressed
separately from mesh connectivity• Geometry data contains more
information than the connectivity data (15 bpv vs 3 bpv)
• Most techniques are lossless
Background• Standard techniques use
quantization and predictive entropy coding– Quantization: 10-14 bpv visually
indistinguishable from the original (“lossless”)
– Prediction rule• Parallelogram rule
[Touma, Gotsman 1998]
Spectral Compression• Consider now an implicit global
prediction rule: Each vertex is the average of all its neighbors
• Laplacian:– Eigenvalues are “frequencies”– Eigenvectors form orthogonal basis
Spectral Compression
Spectral Compression• Encoder
– Compute eigenvectors of L– Project geometry onto the basis vectors (dot
product) to generate coefficients– Quantize these coefficients and entropy code
them• Decoder
– Compute eigenvectors of L– Unpack coefficients– Sum coefficients * eigenvectors to reproduce
the signals
Spectral Compression• Computing eigenvectors
prohibitively expensive for large matrices
• Partition the mesh– MeTiS partitions mesh into balanced
partitions with minimal edge cuts.– Average submesh ~ 500 vertices
Spectral Compression• Visual Metric• Center: 4.1b/v• Right: TG at 4.1b/v (lossless =
6.5b/v)
Spectral Compression• Connectivity Shapes [Isenburg et
al. 2001]
Evaluation• Pros
– Progressive compression/transmission– Capable of compressing more than
traditional methods• Cons
– Expensive• Eigenvectors computed by decoder• Each mesh requires computing new eigenvectors
– Limited to smooth meshes– Edge effects from partitioning
Recent Work• Fixed spectral basis [Gotsman 2001]
– Don’t compute eigenvector basis vectors for each mesh
– Instead, map mesh to another mesh (e.g. 6-regular mesh) for which you have basis functions
– Good results, but small, expected loss of quality
Fixed Spectral Bases
Future Directions• Wavelets (JPEG2000, MPEG4 still
image coder)• Integration of connectivity and
geometry
References• Z. Karni and C. Gotsman. Spectral Compression
of Mesh Geometry. In Proceedings of SIGGRAPH 2000, pp. 279-286, July 2000.
• M. Ben-Chen and C. Gotsman. On the Optimality of Spectral Compression of Mesh Geometry. To appear in ACM transactions on Graphics 2004
• Z. Karni and C.Gotsman. 3D Mesh Compression Using Fixed Spectral Bases. Proceedings of Graphics Interface, Ottawa, June 2001.
• M. Isenburg., S. Gumhold and C. Gotsman. Connectivity Shapes. Proceedings of Visualization, San Diego, October 2001