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Cited by 577

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Adaptively sampled distance fields: a general representation of shape for computer graphics

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Frisken, Sarah F., Perry, Ronald N., Rockwood, Alyn P., Jones, Thouis R.·2000·Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00

Abstract

Adaptively Sampled Distance Fields (ADFs) are a unifying representation of shape that integrate numerous concepts in computer graphics including the representation of geometry and volume data and a broad range of processing operations such as rendering, sculpting, level-of-detail management, surface offsetting, collision detection, and color gamut correction. Its structure is uncomplicated and direct, but is especially effective for quality reconstruction of complex shapes, e.g., artistic and organic forms, precision parts, volumes, high order functions, and fractals. We characterize one implementation of ADFs, illustrating its utility on two diverse applications: 1) artistic carving of fine detail, and 2) representing and rendering volume data and volumetric effects. Other applications are briefly presented.

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Metadata

Authors
Frisken, S. F., Perry, R. N., Rockwood, A. P., & Jones, T. R.
Venue
Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00
Publisher
ACM Press
Published
2000
Pages
249-254
OpenAlex
W2237767100
Cited by
577
References
0
FWCI
13.57
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Adaptively sampled distance fields: a general representation of shape for computer graphics. https://app.cochat.ai/papers/10.1145/344779.344899
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Frisken, S. F., Perry, R. N., Rockwood, A. P., & Jones, T. R. (2000). Adaptively sampled distance fields: a general representation of shape for computer graphics. Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00. https://doi.org/10.1145/344779.344899

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.