The DOI matches its Crossref record.
MESH: measuring errors between surfaces using the Hausdorff distance
Open accessAbstract
This paper proposes an efficient method to estimate the distance between discrete 3D surfaces represented by triangular 3D meshes. The metric used is based on an approximation of the Hausdorff distance, which has been appropriately implemented in order to reduce unnecessary computation and memory usage. Results show that when compared to similar tools, a significant gain in both memory and speed can be achieved.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
High-quality streamable free-viewpoint video
Collet, A., Chuang, M., Sweeney, P., Gillett, D., Evseev, D., Calabrese, D., Hoppe, H., Kirk, A., & Sullivan, S. (2015) · ACM Transactions on Graphics · doi:10.1145/2766945 · Cited by 573
3-D Face Detection, Landmark Localization, and Registration Using a Point Distribution Model
Nair, P., & Cavallaro, A. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2017629 · Cited by 135
Orbital shape in intentional skull deformations and adult sagittal craniosynostoses
Sandy, R., Hennocq, Q., Nysjö, J., Giran, G., Friess, M., & Khonsari, R. H. (2018) · Journal of Anatomy · doi:10.1111/joa.12844 · Cited by 14
Goal-Conditioned Model Simplification for 1-D and 2-D Deformable Object Manipulation
Wang, S., Leonetti, M., & Dogar, M. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3577052 · Cited by 4
“To implement the Error function and find the distance between the simplified mesh and the original one (Algorithm 2, Line 4), we use the Hausdorff distance [54], which is a widely used similarity metric for mesh and image comparison.”
Metadata
- Authors
- Aspert, N., Santa-Cruz, D., & Ebrahimi, T.
- Venue
- Proceedings. IEEE International Conference on Multimedia and Expo
- Publisher
- IEEE
- Published
- 2003-06-25
- Pages
- 705-708
- OpenAlex
- W2169371923
- Cited by
- 718
- References
- 0
- FWCI
- 65.38
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: MESH: measuring errors between surfaces using the Hausdorff distance. https://app.cochat.ai/papers/10.1109/icme.2002.1035879
- The paper
- Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (2003). MESH: measuring errors between surfaces using the Hausdorff distance. Proceedings. IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/icme.2002.1035879
Data sources
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.
