The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
State of the art in high density image matching
Open accessAbstract
Image matching has a history of more than 50 years, with the first experiments performed with analogue procedures for cartographic and mapping purposes. The recent integration of computer vision algorithms and photogrammetric methods is leading to interesting procedures which have increasingly automated the entire image‐based 3D modelling process. Image matching is one of the key steps in 3D modelling and mapping. This paper presents a critical review and analysis of four dense image‐matching algorithms, available as open‐source and commercial software, for the generation of dense point clouds. The eight datasets employed include scenes recorded from terrestrial and aerial blocks, acquired with convergent and normal (parallel axes) images, and with different scales. Geometric analyses are reported in which the point clouds produced with each of the different algorithms are compared with one another and also to ground‐truth data.
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
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457
Cited by
Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015) · Remote Sensing · doi:10.3390/rs70404026 · Cited by 769
Self-citationCunliffe, A. M., Brazier, R. E., & Anderson, K. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.05.019 · Cited by 365
Inzerillo, L., Di Mino, G., & Roberts, R. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.10.010 · Cited by 171
High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery
Carrasco-Escobar, G., Manrique, E., Ruiz-Cabrejos, J., Saavedra, M., Alava, F., Bickersmith, S., Prussing, C., Vinetz, J. M., Conn, J. E., Moreno, M., et al. (2019) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0007105 · Cited by 126
Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019) · Sensors · doi:10.3390/s19143205 · Cited by 76
Metadata
- Authors
- Remondino, F., Spera, M. G., Nocerino, E., Menna, F., & Nex, F.
- Venue
- The Photogrammetric Record
- Publisher
- Wiley
- Published
- 2014-06
- Volume
- 29
- Issue
- 146
- Pages
- 144-166
- ISSN
- 0031-868X (ISSN-L)
- 1477-9730
- OpenAlex
- W1969136216
- Cited by
- 628
- References
- 2
- FWCI
- 90.63
- Type
- journal article
- 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: State of the art in high density image matching. https://app.cochat.ai/papers/10.1111/phor.12063
- The paper
- Remondino, F., Spera, M. G., Nocerino, E., Menna, F., & Nex, F. (2014). State of the art in high density image matching. The Photogrammetric Record. https://doi.org/10.1111/phor.12063
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.
