Subsurface scattering using splat-based diffusion in point-based rendering

Kim, Hyeon-Joong; Bickel, Bernd; Groß, Markus; Choi, Soo-Mi

doi:10.1007/s11432-010-0068-y

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Recently, a surface splatting method for simulating scattering of light in skin has been introduced . This method assumes that the diffusion of light is isotropic; therefore, its effect at a splat can be expressed as a Gaussian distribution applied to the radius of the splat.…”

Section: Resultsmentioning

confidence: 99%

Adaptive surface splatting for facial rendering

Kim

Öztireli

Groß

et al. 2012

Computer Animation & Virtual

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Recent advances in facial scanning technology provide highly detailed faces, including fine wrinkles. However, because of the increasing complexity of the resulting models, it is necessary to reduce the amount of data while preserving small‐scale facial features. In this paper, we propose a new adaptive surface splatting method to reduce the number of splats by optimizing the size and shape of splats using geometric and color information. Using the optimized splats, we can improve the rendering quality, especially in the visually sensitive feature areas. Our adaptive surface splatting is very effective to render massive facial data on tablet PCs or smartphones. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Adaptive surface splatting for facial rendering

Kim

Öztireli

Groß

et al. 2012

Computer Animation & Virtual

Self Cite

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“…According to our experiment, the simple selection of w=1 for skin and w=0.5 for facial feature areas works well in practice for generating perceptually correct renderings. For the rendering stage, we use a splat-based representation [5], in which point samples are converted into surface splats with the orientation and spatial extent optimized to fit the geometric and facial features. For efficiency, each splat is represented by a circular disk that is centered near the sample point position p, oriented orthogonal to the normal n, and has the radius r and the splat color c. The scale factor of the radius r of each splat is determined in a locally adaptive way.…”

Section: Feature-adaptive Simplification Of 3d Facesmentioning

confidence: 99%

Feature Detection and Simplification of 3D Face Data with Facial Expressions

Kim¹,

Kim²,

Choi³

et al. 2012

ETRI J

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We propose an efficient framework to realistically render 3D faces with a reduced set of points. First, a robust active appearance model is presented to detect facial features in the projected faces under different illumination conditions. Then, an adaptive simplification of 3D faces is proposed to reduce the number of points, yet preserve the detected facial features. Finally, the point model is rendered directly, without such additional processing as parameterization of skin texture. This fully automatic framework is very effective in rendering massive facial data on mobile devices.

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“…In our implementation, the position of the proxy is simply chosen as the centroid point of the sample points in the cluster, that is, p = 1 k i p i . However, comparing with the object-space circular surfel representations for simplified models, the elliptical splats can in general provide a better surface coverage [5,28,34]. Perspective accurate splatting of circular or elliptical splats has been treated in [35][36][37][38] using shaders and α-textured polygons respectively.…”

Section: Simplified Surfel Generation: Generating the Representative Pointsmentioning

confidence: 99%

“…Then, smooth rendering of the elliptical surfels is performed analogously to standard point-rendering methods using a three-pass approach with ε-z-buffer culling and blending between overlapping surfels [30,31,34], although single-pass methods such as [40] could be used as well.…”

Section: Rendering Of Simplified Point Models By Ellipse-splattingmentioning

confidence: 99%

Feature sensitive re-sampling of point set surfaces with Gaussian spheres

Miao

Bösch

Pajarola

et al. 2012

Sci. China Inf. Sci.

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Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many computer graphics and geometric modeling applications. Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere, an adaptive re-sampling framework for point set surfaces is presented in this paper, which includes a naive sampling step by index propagation and a novel cluster optimization step by normalized rectification. Our proposed re-sampling scheme can generate non-uniformly distributed discrete sample points for the underlying point sets in a feature sensitive manner. The intrinsic geometric features of the underlying point set surfaces can be preserved efficiently due to our adaptive re-sampling scheme. A novel splat rendering technique is adopted to illustrate the efficiency of our re-sampling scheme. Moreover, a numerical error statistics and surface reconstruction for simplified models are also given to demonstrate the effectiveness of our algorithm in term of the simplified quality of the point set surfaces.

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Subsurface scattering using splat-based diffusion in point-based rendering

Cited by 5 publications

References 15 publications

Adaptive surface splatting for facial rendering

Adaptive surface splatting for facial rendering

Feature Detection and Simplification of 3D Face Data with Facial Expressions

Feature sensitive re-sampling of point set surfaces with Gaussian spheres

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