Arithmetic coding for data compression
Communications of the ACM
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Geometric compression through topological surgery
ACM Transactions on Graphics (TOG)
Real time compression of triangle mesh connectivity
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Tetrahedral mesh compression with the cut-border machine
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Face fixer: compressing polygon meshes with properties
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Progressive geometry compression
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Spectral compression of mesh geometry
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Geometric compression for interactive transmission
Proceedings of the conference on Visualization '00
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Compressing polygon mesh geometry with parallelogram prediction
Proceedings of the conference on Visualization '02
Edgebreaker: Connectivity Compression for Triangle Meshes
IEEE Transactions on Visualization and Computer Graphics
Compressing the property mapping of polygon meshes
Graphical Models - Pacific graphics 2001
Single Resolution Compression of Arbitrary Triangular Meshes with Properties
DCC '99 Proceedings of the Conference on Data Compression
Out-of-core compression for gigantic polygon meshes
ACM SIGGRAPH 2003 Papers
High-pass quantization for mesh encoding
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Compressing hexahedral volume meshes
Graphical Models - Special issue on Pacific graphics 2002
Streaming compression of tetrahedral volume meshes
GI '06 Proceedings of Graphics Interface 2006
Streaming compression of triangle meshes
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Progressive lossless mesh compression via incremental parametric refinement
SGP '09 Proceedings of the Symposium on Geometry Processing
Compressing the incompressible with ISABELA: in-situ reduction of spatio-temporal data
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Providing flexible tradeoff for provenance tracking
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Lossless compression of variable-precision floating-point buffers on GPUs
I3D '12 Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
The alpha parallelogram predictor: A lossless compression method for motion capture data
Information Sciences: an International Journal
ARC'13 Proceedings of the 9th international conference on Reconfigurable Computing: architectures, tools, and applications
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The size of geometric data sets in scientific and industrial applications is constantly increasing. Storing surface or volume meshes in standard uncompressed formats results in large files that are expensive to store and slow to load and transmit. Scientists and engineers often refrain from using mesh compression because currently available schemes modify the mesh data. While connectivity is encoded in a lossless manner, the floating-point coordinates associated with the vertices are quantized onto a uniform integer grid to enable efficient predictive compression. Although a fine enough grid can usually represent the data with sufficient precision, the original floating-point values will change, regardless of grid resolution. In this paper we describe a method for compressing floating-point coordinates with predictive coding in a completely lossless manner. The initial quantization step is omitted and predictions are calculated in floating-point. The predicted and the actual floating-point values are broken up into sign, exponent, and mantissa and their corrections are compressed separately with context-based arithmetic coding. As the quality of the predictions varies with the exponent, we use the exponent to switch between different arithmetic contexts. We report compression results using the popular parallelogram predictor, but our approach will work with any prediction scheme. The achieved bit-rates for lossless floating-point compression nicely complement those resulting from uniformly quantizing with different precisions.