Image-Space Subsurface Scattering for Interactive Rendering of Deformable Translucent Objects

2009 ◽  
Vol 29 (1) ◽  
pp. 66-78 ◽  
Author(s):  
Musawir A. Shah ◽  
Jaakko Konttinen ◽  
Sumanta Pattanaik
2011 ◽  
Vol 10 (1) ◽  
pp. 45-51
Author(s):  
Hiroyuki Kubo ◽  
Yoshinori Dobashi ◽  
Shigeo Morishima

Simulating sub-surface scattering is one of the most effective ways to realistically synthesize translucent materials such as marble, milk or human skin. We have developed a curvature-dependent reflectance function (CDRF) which mimics the presence of subsurface scattering. In this approach, we provide only a single parameter that represents the intensity of theincident light scattered in a translucent material. The parameter is not only provided by curve-fitting to a simulated data-set, but also manipulated by an artist. Furthermore, this approach is easily implementable on a GPU and does not require any complicated pre-processing and multi-pass rendering as is often the case in this area of research.


Author(s):  
T. Yanaka ◽  
K. Shirota

It is significant to note field aberrations (chromatic field aberration, coma, astigmatism and blurring due to curvature of field, defined by Glaser's aberration theory relative to the Blenden Freien System) of the objective lens in connection with the following three points of view; field aberrations increase as the resolution of the axial point improves by increasing the lens excitation (k2) and decreasing the half width value (d) of the axial lens field distribution; when one or all of the imaging lenses have axial imperfections such as beam deflection in image space by the asymmetrical magnetic leakage flux, the apparent axial point has field aberrations which prevent the theoretical resolution limit from being obtained.


Author(s):  
W.J.T. Mitchell

Przekład tekstu "Image, Space, and Rewolution. The Arts of Occupation", który ukazał się w "Critical Inquiry" (2012, nr 39)


Author(s):  
Tiantian Xie ◽  
Marc Olano ◽  
Brian Karis ◽  
Krzysztof Narkowicz

In real-time applications, it is difficult to simulate realistic subsurface scattering with differing degrees translucency. Burley's reflectance approximation by empirically fitting the diffusion profile as a whole makes it possible to achieve realistic looking subsurface scattering for different translucent materials in screen space. However, achieving a physically correct result requires real-time Monte Carlo sampling of the analytic importance function per pixel per frame, which seems prohibitive to achieve. In this paper, we propose an approximation of the importance function that can be evaluated in real-time. Since subsurface scattering is more pronounced in certain regions (e.g., with light gradient change), we propose an adaptive sampling method based on temporal variance to lower the required number of samples. We propose a one phase adaptive sampling pass that is unbiased, and able to adapt to scene changes due to motion and lighting. To further improve the quality, we explore temporal reuse with a guiding pass prior to the final temporal anti-aliasing (TAA) phase that further improves the quality. Our local guiding pass does not constrain the TAA implementation, and only requires one additional texture to be passed between frames. Our proposed variance-guided algorithm has the potential to make stochastic sampling algorithm effective for real-time rendering.


Author(s):  
Patrick Knöbelreiter ◽  
Thomas Pock

AbstractIn this work, we propose a learning-based method to denoise and refine disparity maps. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. To this end, we can visualize and interpret the learned filters and activation functions and prove the increased reliability of the predicted pixel-wise confidence maps. Furthermore, the optimization based structure of our refinement module allows us to compute eigen disparity maps, which reveal structural properties of our refinement module. The efficiency of our method is demonstrated on the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.


2008 ◽  
Vol 35 (9) ◽  
pp. 4251-4261 ◽  
Author(s):  
Anne B. Benincasa ◽  
Logan W. Clements ◽  
S. Duke Herrell ◽  
Robert L. Galloway

Author(s):  
CAIXIA DENG ◽  
YULING QU ◽  
LIJUAN GU

In this paper, Journe wavelet function is introduced as a wavelet generating function. The expression of reproducing kernel function for the image space of this wavelet transform is obtained based on the fact that the image space of the wavelet transform is a reproducing kernel Hilbert space. Then the isometric identity of Journe wavelet transform is obtained. The connections between the image space of the wavelet transform and the image space of the known reproducing kernel space are established by the theories of reproducing kernel. The properties and the structures of the image space of the wavelet transform can be characterized by the properties and the structures of the image space of the known reproducing kernel space. Using the ideas of reproducing kernel, we consider there are relations between the wavelet transform and the sampling theorem. Meanwhile, the approximations in sampling theorems is shown and the truncation error is given. This provides a theoretical basis for us to study the image space of the general wavelet transform and broadens the scope of application of theories of the reproducing kernel space.


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