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Metro: Measuring Error on Simplified Surfaces
Abstract
This paper presents a new tool, Metro, designed to compensate for a deficiency in many simplification methods proposed in literature. Metro allows one to compare the difference between a pair of surfaces (e.g. a triangulated mesh and its simplified representation) by adopting a surface sampling approach. It has been designed as a highly general tool, and it does no assumption on the particular approach used to build the simplified representation. It returns both numerical results (meshes areas and volumes, maximum and mean error, etc.) and visual results, by coloring the input surface according to the approximation error.
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Cited by
Morpho and Rvcg – Shape Analysis in R
Schlager, S. (2017) · Statistical Shape and Deformation Analysis · doi:10.1016/b978-0-12-810493-4.00011-0 · Cited by 894
Schreiner, J., Asirvatham, A., Praun, E., & Hoppe, H. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015812 · Cited by 265
3D model reconstruction using neural gas accelerated on GPU
Orts-Escolano, S., Garcia-Rodriguez, J., Serra-Perez, J. A., Jimeno-Morenilla, A., Garcia-Garcia, A., Morell, V., & Cazorla, M. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.03.042 · Cited by 8
Metadata
- Authors
- Cignoni, P., Rocchini, C., & Scopigno, R.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 1998-06
- Volume
- 17
- Issue
- 2
- Pages
- 167-174
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2006502673
- Cited by
- 1,501
- References
- 0
- FWCI
- 25.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Metro: Measuring Error on Simplified Surfaces. https://app.cochat.ai/papers/10.1111/1467-8659.00236
- The paper
- Cignoni, P., Rocchini, C., & Scopigno, R. (1998). Metro: Measuring Error on Simplified Surfaces. Computer Graphics Forum. https://doi.org/10.1111/1467-8659.00236
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
