__abstract:__

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.

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.

__main contributions:__

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