The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automatic creation of semantically rich 3D building models from laser scanner data
Overview
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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
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Citations
Cites
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
Cited by
Dimitrov, A., & Golparvar-Fard, M. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.11.002 · Cited by 261
Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review
Hosamo, H. H., & Hosamo, M. H. (2022) · Advances in Civil Engineering · doi:10.1155/2022/2194949 · Cited by 103
A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management
Cheng, R., Hou, L., & Xu, S. (2023) · Buildings · doi:10.3390/buildings13051143 · Cited by 94
The role of geometric characterization in supporting structural steel reuse decisions
Yeung, J., Walbridge, S., & Haas, C. (2015) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2015.08.017 · Cited by 52
Scanning and data capturing for BIM-supported resources assessment: a case study
Kovacic, I., & Honic, M. (2021) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2021.032 · Cited by 40
Metadata
- Authors
- Xiong, X., Adan, A., Akinci, B., & Huber, D.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2013-05
- Volume
- 31
- Pages
- 325-337
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W2033552406
- Cited by
- 703
- References
- 1
- FWCI
- 73.00
- Type
- journal article
- Language
- en
- Open access
- closed
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automatic creation of semantically rich 3D building models from laser scanner data. https://app.cochat.ai/papers/10.1016/j.autcon.2012.10.006
- The paper
- Xiong, X., Adan, A., Akinci, B., & Huber, D. (2013). Automatic creation of semantically rich 3D building models from laser scanner data. Automation in Construction. https://doi.org/10.1016/j.autcon.2012.10.006
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.
