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Cone-Traced Supersampling With Subpixel Edge Reconstruction.

Authors :
Chubarau A
Zhao Y
Rao R
Nowrouzezahrai D
Kry PG
Source :
IEEE transactions on visualization and computer graphics [IEEE Trans Vis Comput Graph] 2024 Sep; Vol. 30 (9), pp. 6421-6432. Date of Electronic Publication: 2024 Jul 31.
Publication Year :
2024

Abstract

While signed distance fields (SDFs) in theory offer infinite level of detail, they are typically rendered using the sphere tracing algorithm at finite resolutions, which causes the common rasterized image synthesis problem of aliasing. Most existing optimized antialiasing solutions rely on polygon mesh representations; SDF-based geometry can only be directly antialiased with the computationally expensive supersampling or with post-processing filters that may produce undesirable blurriness and ghosting. In this work, we present cone-traced supersampling (CTSS), an efficient and robust spatial antialiasing solution that naturally complements the sphere tracing algorithm, does not require casting additional rays per pixel or offline pre-filtering, and can be easily implemented in existing real-time SDF renderers. CTSS performs supersampling along the traced ray near surfaces with partial visibility - object contours - identified by evaluating cone intersections within a pixel's view frustum. We further introduce subpixel edge reconstruction (SER), a technique that extends CTSS to locate and resolve complex pixels with geometric edges in relatively flat regions, which are otherwise undetected by cone intersections. Our combined solution relies on a specialized sampling strategy to minimize the number of shading computations and correlates sample visibility to aggregate the samples. With comparable antialiasing quality at significantly lower computational cost, CTSS is a reliable practical alternative to conventional supersampling.

Details

Language :
English
ISSN :
1941-0506
Volume :
30
Issue :
9
Database :
MEDLINE
Journal :
IEEE transactions on visualization and computer graphics
Publication Type :
Academic Journal
Accession number :
38096100
Full Text :
https://doi.org/10.1109/TVCG.2023.3343166