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Microfacet-based Normal Mapping for Robust Monte Carlo Path Tracing

delete2017-11-20
delete19
PRE
AI
V
Vincent Schüßler *
E
Eric Heitz
J
Johannes Hanika
C
Carsten Dachsbacher
DOI:10.1145/3130800.3130806delete
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摘要

摘要

En 中文
Normal mapping enhances the amount of visual detail of surfaces by using shading normals that deviate from the geometric normal. However, the resulting surface model is geometrically impossible and normal mapping is thus often considered a fundamentally flawed approach with unavoidable problems for Monte Carlo path tracing, such as asymmetry, back-facing normals, and energy loss arising from this incoherence. These problems are usually sidestepped in real-time renderers, but they cannot be fixed robustly in a path tracer: normal mapping breaks either the appearance (black fringes, energy loss) or the integrator (different forward and backward light transport); in practice, workarounds and tweaked normal maps are often required to hide artifacts. We present microfacet-based normal mapping, an alternative way of faking geometric details without corrupting the robustness of Monte Carlo path tracing. It takes the same input data as classic normal mapping and works with any input BRDF. Our idea is to construct a geometrically valid microfacet surface made of two facets per shading point: the one given by the normal map at the shading point and an additional facet that compensates for it such that the average normal of the microsurface equals the geometric normal. We derive the resulting microfacet BRDF and show that it mimics geometric detail in a plausible way, although it does not replicate the appearance of classic normal mapping. However, our microfacet-based normal mapping model is well-defined, symmetric, and energy conserving, and thus yields identical results with any path tracing algorithm (forward, backward, or bidirectional).
Keyword:
Normal mapping
path tracing
microfacet theory
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期刊

ACM Transactions on Graphics 封面图
ACM Transactions on Graphics
IF:
9.5
论文数:
4.8K
被引数:
3.6W

机构

K
karlsruhe institute of technology
学者数:
2.0W
论文数: 1.5W
被引数: 23
H
Helmholtz Association
学者数:
13.2W
论文数: 10.7W
被引数: 145
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