Fast Spheres, Shadows, Textures, Transparencies, and Image Enhancements in Pixel-Planes

Fuchs, Henry; Goldfeather, Jack; Hultquist, Jeff P.; Spach, Susan; Austin, John D.; Brooks, Frederick P.; Eyles, John; Poulton, John W.

doi:10.1007/978-3-642-46514-7_7

Cited by 52 publications

(39 citation statements)

References 7 publications

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“…For each triangle T ′ in {T ′ n + T ′ e }, we apply the classical edge walking algorithm [8], which is usually used in the rasterisation procedure of a 3D graphics pipeline, to obtain texel samples. Number of texel samples obtained increases with the triangle area.…”

Section: -Stepmentioning

confidence: 99%

Visual saliency guided textured model simplification

Yang

Wang

et al. 2015

Vis Comput

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and use texture maps modelling most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to generate a unified saliency map for identifying visually important object features. Results show our method offers a better object rendering quality than existing methods.

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Section: -Stepmentioning

confidence: 99%

Visual saliency guided textured model simplification

Yang

Wang

et al. 2015

Vis Comput

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“…Screen-door transparency replaces transparent surfaces with a set of pixels that are fully on or off [Fuchs et al 1985]. The choice of stipple patterns can be optimized so they are more visually pleasing to viewers [Mulder et al 1998].…”

Section: Prior Workmentioning

confidence: 99%

Stochastic transparency

Enderton¹,

Sintorn

Shirley³

et al. 2010

Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

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Stochastic transparency provides a unified approach to orderindependent transparency, anti-aliasing, and deep shadow maps. It augments screen-door transparency using a random sub-pixel stipple pattern, where each fragment of transparent geometry covers a random subset of pixel samples of size proportional to alpha. This results in correct alpha-blended colors on average, in a single render pass with fixed memory size and no sorting, but introduces noise. We reduce this noise by an alpha correction pass, and by an accumulation pass that uses a stochastic shadow map from the camera. At the pixel level, the algorithm does not branch and contains no read-modify-write loops other than traditional z-buffer blend operations. This makes it an excellent match for modern massively parallel GPU hardware. Stochastic transparency is very simple to implement and supports all types of transparent geometry, able without coding for special cases to mix hair, smoke, foliage, windows, and transparent cloth in a single scene.

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“…The Pixel-Planes [Fuchs85] and Pineda [Pineda88] scan conversion algorithms do parallelize well. In both of these algorithms, each triangle edge is represented by a linear edge function.…”

Section: Prior Workmentioning

confidence: 99%

“…Following the Pixel-Planes and Pineda algorithms [Fuchs85,Pineda88], we compute a linear function for each edge of the triangle. This function is positive on inside of the edge and negative on the outside.…”

Section: Edge Functionmentioning

confidence: 99%

Triangle scan conversion using 2D homogeneous coordinates

Olano¹,

Greer

1997

Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Workshop on Graphics Hardware - HWWS '97

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We present a new triangle scan conversion algorithm that works entirely in homogeneous coordinates. By using homogeneous coordinates, the algorithm avoids costly clipping tests which make pipelining or hardware implementations of previous scan conversion algorithms difficult. The algorithm handles clipping by the addition of clip edges, without the need to actually split the clipped triangle. Furthermore, the algorithm can render true homogeneous triangles, including external triangles that should pass through infinity with two visible sections. An implementation of the algorithm on Pixel-Planes 5 runs about 33% faster than a similar implementation of the previous algorithm.

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Fast Spheres, Shadows, Textures, Transparencies, and Image Enhancements in Pixel-Planes

Cited by 52 publications

References 7 publications

Visual saliency guided textured model simplification

Visual saliency guided textured model simplification

Stochastic transparency

Triangle scan conversion using 2D homogeneous coordinates

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