The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
Bayesian Fusion Inspired 3D Reconstruction via LiDAR-Stereo Camera Pair
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Depth information from a LiDAR and a stereo-pair has been demonstrated to complement each other and has been effectively used to upsample depth information while improving the accuracy of the assimilated 3D point-cloud as shown in19.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [19]
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information
Hirschmuller, H. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.56 · Cited by 1,698
Gaussian Process Regression for Materials and Molecules
Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Csányi, G. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00022 · Cited by 1,221
A pixel dissimilarity measure that is insensitive to image sampling
Birchfield, S., & Tomasi, C. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.677269 · Cited by 585
Remarks on multi-output Gaussian process regression
Liu, H., Cai, J., & Ong, Y.-S. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.12.034 · Cited by 294
Method for measuring stereo camera depth accuracy based on stereoscopic vision
Kytö, M., Nuutinen, M., & Oittinen, P. T. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.872015 · Cited by 105
Real-time probabilistic fusion of sparse 3D LIDAR and dense stereo
Maddern, W., & Newman, P. M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759342 · Cited by 95
Multisensor data fusion via Gaussian process models for dimensional and geometric verification
Colosimo, B. M., Pacella, M., & Senin, N. (2014) · Precision Engineering · doi:10.1016/j.precisioneng.2014.11.011 · Cited by 79
High-Precision Depth Estimation with the 3D LiDAR and Stereo Fusion
Park, K., Kim, S., & Sohn, K. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461048 · Cited by 73
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation
Choe, J., Joo, K., Imtiaz, T., & Kweon, I. S. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3068712 · Cited by 59
Research works printed but not linked: 1
- Rasmussen, C.E., Williams, C.K., et al.: Gaussian Processes for Machine Learning, vol. 1. Springer, Cham (2006)
Cited by
Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning
Kim, I., & Singh, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-81377-z · Cited by 6
Self-citation3D point cloud denoising via Gaussian processes regression
Kim, I., & Singh, S. K. (2025) · The Visual Computer · doi:10.1007/s00371-025-04049-7 · Cited by 5
Self-citation
Metadata
- Authors
- Kim, I., & Singh, S.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- Corresponding author
- Kim, Ickbum
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 299-310
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4389272164
- Cited by
- 2
- References
- 15
- Citations by year
- 2025: 1 · 2024: 1
- FWCI
- 0.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- 3D reconstruction, Gaussian process regression, depth map fusion, point cloud sparsity, low-cost LiDAR, Bayesian inference
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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.
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bayesian Fusion Inspired 3D Reconstruction via LiDAR-Stereo Camera Pair. https://app.cochat.ai/papers/10.1007/978-3-031-47966-3_23
- The paper
- Kim, I., & Singh, S. (2023). Bayesian Fusion Inspired 3D Reconstruction via LiDAR-Stereo Camera Pair. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-47966-3_23
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.
