GPU-accelerated Adaptively Sampled Distance Fields

Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we pro...

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Vydáno v:2008 IEEE International Conference on Shape Modeling and Applications s. 171 - 178
Hlavní autoři: Bastos, T., Celes, W.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2008
Témata:
ADFs
Application software
Computer graphics
Computer vision
Data structures
GPU algorithms
Hardware
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Curve
I.3.6 [Computer Graphics]: Methodology and Techniques-Graphics data structures and data types
implicit surface rendering
Physics
Proposals
Rendering (computer graphics)
Shape
solid and object representations
surface
Surface reconstruction
ISBN:9781424422609, 1424422604
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Abstract Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we propose a strategy to represent and reconstruct ADFs on modern graphics hardware (GPUs). We employ a 3D hashing scheme to store the underlying data structure and try to balance the tradeoff between memory requirements and reconstruction efficiency. To render ADFs on GPU, we use a general-purpose ray-casting technique based on sphere tracing, which guarantees the reconstruction of fine details. We also present a way to overcome the Cl discontinuities inherent to ADFs and efficiently reconstruct smooth surface normals across cell boundaries. The effectiveness of our proposal is demonstrated for isosurface rendering and morphing.
AbstractList Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we propose a strategy to represent and reconstruct ADFs on modern graphics hardware (GPUs). We employ a 3D hashing scheme to store the underlying data structure and try to balance the tradeoff between memory requirements and reconstruction efficiency. To render ADFs on GPU, we use a general-purpose ray-casting technique based on sphere tracing, which guarantees the reconstruction of fine details. We also present a way to overcome the Cl discontinuities inherent to ADFs and efficiently reconstruct smooth surface normals across cell boundaries. The effectiveness of our proposal is demonstrated for isosurface rendering and morphing.
Author Celes, W.
Bastos, T.
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  givenname: W.
  surname: Celes
  fullname: Celes, W.
  organization: Dept. of Comput. Sci., Pontifical Catholic Univ. of Rio de Janeiro, Rio de Janeiro
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Snippet Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics,...
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StartPage 171
SubjectTerms ADFs
Application software
Computer graphics
Computer vision
Data structures
GPU algorithms
Hardware
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Curve
I.3.6 [Computer Graphics]: Methodology and Techniques-Graphics data structures and data types
implicit surface rendering
Physics
Proposals
Rendering (computer graphics)
Shape
solid and object representations
surface
Surface reconstruction
Title GPU-accelerated Adaptively Sampled Distance Fields
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