The geometric data sets found in scientific and industrial applications are often very detailed. Storing them using standard uncompressed formats results in large files that are expensive to store and slow to load and transmit. Many efficient mesh compression techniques have been proposed, but scientists and engineers often refrain from using them because they 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 for 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 how to compress floating-point coordinates using 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 then 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. Although we report compression results using the popular parallelogram predictor, our approach works with any prediction scheme. The achieved bit-rates for lossless floating-point compression nicely complement those resulting from uniformly quantizing with different precisions.