The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Hierarchical face clustering on polygonal surfaces
Abstract
Many graphics applications, and interactive systems in particular, rely on hierarchical surface representations to efficiently process very complex models. Considerable attention has been focused on hierarchies of surface approximations and their construction via automatic surface simplification. Such representations have proven effective for adapting the level of detail used in real time display systems. However, other applications such as raytracing, collision detection, and radiosity benefit from an alternative multiresolution framework: hierarchical partitions of the original surface geometry. We present a new method for representing a hierarchy of regions on a polygonal surface which partition that surface into a set of face clusters. These clusters, which are connected sets of faces, represent the aggregate properties of the original surface a different scales rather than providing geometric approximations of varying complexity. We also describe the combination of an effective error metric and a novel algorithm for constructing these hierarchies.
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Cites
OBBTree: a hierarchical structure for rapid interference detection
Gottschalk, S., Lin, M. C., & Manocha, D. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237244 · Cited by 2,081
Cited by
The State of the Art in Interactive Global Illumination
Ritschel, T., Dachsbacher, C., Grosch, T., & Kautz, J. (2012) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2012.02093.x · Cited by 132
Metadata
- Authors
- Garland, M., Willmott, A., & Heckbert, P. S.
- Venue
- Proceedings of the 2001 symposium on Interactive 3D graphics
- Publisher
- ACM
- Published
- 2001-03
- Pages
- 49-58
- OpenAlex
- W1977387474
- Cited by
- 375
- References
- 1
- FWCI
- 10.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.