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 t...
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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).
The proceedings contain 19 papers. The topics discussed include: terrain models for mass movement erosion;morphological analysis from images of hyphal growth using a fractional dynamic model;memory efficient surface r...
ISBN:
(纸本)9783905673838
The proceedings contain 19 papers. The topics discussed include: terrain models for mass movement erosion;morphological analysis from images of hyphal growth using a fractional dynamic model;memory efficient surface reconstruction based on self organizing maps;pixel-level algorithms for drawing curves;building a video wall for earth observation data;dynamic video face transformation using multilinear and autoregressive models;visualization of blockplay in early childhood;airborne ultrasound pulse force device for palpation simulation;simple and efficient normal encoding with error bounds;a low-cost single-pixel thermographic camera;interactive computer visualizations of time and place;model and visualize the relationship between energy consumption and temperature distribution in cold rooms;visual saliency for smell impulses and application to selective rendering;and advantages of 3D extraction and spatial awareness within a videoconferencing environment.
Experimental teaching is an important component of the whole teaching process in colleges and universities and is an important process of cultivating highquality talent. Linking theory with practice and cultivating pr...
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Principal component analysis is a ubiquitous method in parametric appearance modeling for describing dependency and variance in a data set. The method requires that the observed data be Gaussian-distributed. We show t...
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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 t...
详细信息
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).
location and decreases the power consumption by 2 x. " computergraphics: Light fields enable new Sparse Fourier Transform reduces NMR (Nuclear Magnetic Resonance) experiment time by 16 x (e.g. from. It delivers ...
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location and decreases the power consumption by 2 x. " computergraphics: Light fields enable new Sparse Fourier Transform reduces NMR (Nuclear Magnetic Resonance) experiment time by 16 x (e.g. from. It delivers a 0.75 million point Sparse Fourier Transform chip that consumes 40 x less power than prior FFT VLSI implementations.
Since being introduced to graphics in the 1980s, Monte Carlo sampling and integration has become the cornerstone of most modern rendering algorithms. Originally introduced to combat the effect of aliasing when estimat...
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ISBN:
(纸本)9781450342896
Since being introduced to graphics in the 1980s, Monte Carlo sampling and integration has become the cornerstone of most modern rendering algorithms. Originally introduced to combat the effect of aliasing when estimating pixels values, Monte Carlo has since become a more general tool for solving complex, multi-dimensional integration problems in rendering. In this context, MC integration involves sampling a function at various stochastically placed points to approximate an integral, e.g. the radiance through a pixel integrated across the multi-dimensional space of possible light transport paths. Unfortunately, this estimation is error-prone, and the visual manifestation of this error depends critically on the properties of the integrand, placement of the stochastic sample points used, and the type of problem (integration vs. reconstruction) that is being solved with these samples. We describe how errors present in rendered images may be analyzed as a function of the spectral (Fourier domain) statistics of the underlying sampling patterns fed to the renderer. Fourier analysis, along with the Nyquist theorem, has long been used in graphics to motivate more intelligent sampling strategies which try to minimize errors due to noise and aliasing in the pixel reconstruction problem. Only more recently, however, has the community started to apply these same Fourier tools to analyze error in the Monte Carlo integration problem. Loosely speaking, in the context of rendering a 2D image, these two problems are concerned with errors introduced across pixels (reconstruction) vs. the errors introduced within any individual pixel (integration). In this course, we focus on the latter, and survey the recent developments and insights that Fourier analyses have provided about the magnitude and convergence rate of Monte Carlo integration error. We provide a historical perspective of Monte Carlo in graphics, review the necessary mathematical background, summarize the most recent developm
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