Adaptive Temporal Sampling for Volumetric Path Tracing of Medical Data. (30th July 2019)

• Record Type: Journal Article
• Authors: Martschinke, J.; Hartnagel, S.; Keinert, B.; Engel, K.; Stamminger, M.
• Is Part Of: Computer graphics forum. Volume 38:Number 4(2019)
• Page Start: 67
• Page End: 76
• Publication Date: 2019-07-30
• Subjects: CCS Concepts -- Human‐centered computing → Scientific visualization -- Computing methodologies → Ray tracing -- Applied computing → Health informatics; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13771
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Monte‐Carlo path tracing techniques can generate stunning visualizations of medical volumetric data. In a clinical context, such renderings turned out to be valuable for communication, education, and diagnosis. Because a large number of computationally expensive lighting samples is required to converge to a smooth result, progressive rendering is the only option for interactive settings: Low‐sampled, noisy images are shown while the user explores the data, and as soon as the camera is at rest the view is progressively refined. During interaction, the visual quality is low, which strongly impedes the user's experience. Even worse, when a data set is explored in virtual reality, the camera is never at rest, leading to constantly low image quality and strong flickering. In this work we present an approach to bring volumetric Monte‐Carlo path tracing to the interactive domain by reusing samples over time. To this end, we transfer the idea of temporal antialiasing from surface rendering to volume rendering. We show how to reproject volumetric ray samples even though they cannot be pinned to a particular 3D position, present an improved weighting scheme that makes longer history trails possible, and define an error accumulation method that downweights less appropriate older samples. Furthermore, we exploit reprojection information to adaptively determine the number of newly generated path tracing samples for each individual pixel. Our approach is designed for static, medical data with both volumetric and surface‐like structures. It achieves good‐quality volumetric Monte‐Carlo renderings with only little noise, and is also usable in a VR context.