What every computer scientist should know about floating-point arithmetic
ACM Computing Surveys (CSUR)
Controlled precision volume integration
VVS '92 Proceedings of the 1992 workshop on Volume visualization
A comparison of normal estimation schemes
VIS '97 Proceedings of the 8th conference on Visualization '97
Opacity-weighted color interpolation, for volume sampling
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
A practical evaluation of popular volume rendering algorithms
VVS '00 Proceedings of the 2000 IEEE symposium on Volume visualization
Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
Optical Models for Direct Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Evaluation and Design of Filters Using a Taylor Series Expansion
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
A Next Step: Visualizing Errors and Uncertainty
IEEE Computer Graphics and Applications
An evaluation of reconstruction filters for volume rendering
VIS '94 Proceedings of the conference on Visualization '94
Squeeze: numerical-precision-optimized volume rendering
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
HDR VolVis: High Dynamic Range Volume Visualization
IEEE Transactions on Visualization and Computer Graphics
Real-time Volume Graphics
IEEE Transactions on Visualization and Computer Graphics
MPFR: A multiple-precision binary floating-point library with correct rounding
ACM Transactions on Mathematical Software (TOMS)
Conjoint Analysis to Measure the Perceived Quality in Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
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In this paper we study the comprehensive effects on volume rendered images due to numerical errors caused by the use of finite precision for data representation and processing. To estimate actual error behavior we conduct a thorough study using a volume renderer implemented with arbitrary floating-point precision. Based on the experimental data we then model the impact of floating-point pipeline precision, sampling frequency and fixedpoint input data quantization on the fidelity of rendered images. We introduce three models, an average model, which does not adapt to different data nor varying transfer functions, as well as two adaptive models that take the intricacies of a new data set and transfer function into account by adapting themselves given a few different images rendered. We also test and validate our models based on new data that was not used during our model building.