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View-Dependent Visibility Optimization for Monte Carlo Volume Visualization

Lerzer, Nathan ORCID iD icon 1; Dachsbacher, Carsten 1
1 Institut für Visualisierung und Datenanalyse (IVD), Karlsruher Institut für Technologie (KIT)

Abstract:

Compared to classic ray marching-based approaches, Monte Carlo ray tracing for volume visualization can provide faster frame times through progressive rendering, improved image quality, and allows for advanced illumination models more easily. Techniques such as the view-dependent optimization of visibility and illumination of important regions, however, have been formulated for ray marching and rely on stepwise sampling along rays, and are thus incompatible with free-flight distance sampling of state-of-the-art Monte Carlo methods. In this paper we derive such a view-dependent optimization for Monte Carlo ray tracing where the visibility to the camera, the illumination and opacity of important regions is optimized for both single and multiple scattering rendering. For this we define a post-interpolative importance function, introduce an efficient data structure to sample, approximate and optimize the integrated extinction along rays, and devise an efficient Monte Carlo estimator for interactive visualization. Our method enables view-dependent visibility optimization with moderate memory overhead and unbiased, progressive Monte Carlo volume visualization. ... mehr

Zugehörige Institution(en) am KIT Institut für Visualisierung und Datenanalyse (IVD)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2025
Sprache Englisch
Identifikator KITopen-ID: 1000180574
Verlag John Wiley and Sons
Umfang 11 S.
Bemerkung zur Veröffentlichung Die DOI funktioniert aktuell noch nicht, da die Publikation erst im Mai zur Eurographics 2025 erscheint
Schlagwörter CCS Concepts: Human-centered computing → Scientific visualization
Nachgewiesen in Dimensions
OpenAlex
Web of Science
Scopus

Seitenaufrufe: 72
seit 31.03.2025
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