The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
3D is here: Point Cloud Library (PCL)
Abstract
With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library - http://pointclouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.
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
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Fast Point Feature Histograms (FPFH) for 3D registration
Rusu, R. B., Blodow, N., & Beetz, M. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152473 · Cited by 3,878
Self-citationFAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
(2009) · Proceedings of the Fourth International Conference on Computer Vision Theory and Applications · doi:10.5220/0001787803310340 · Cited by 2,640
The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics
Schroeder, W. J., & Lorenson, B. (1997) · Cited by 1,882
Towards 3D Point cloud based object maps for household environments
Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.005 · Cited by 1,084
Self-citationIntel threading building blocks : outfitting C++ for multi-core processor parallelism
Reinders, J. R. (2007) · Cited by 708
Point feature extraction on 3D range scans taking into account object boundaries
Steder, B., Rusu, R. B., Konolige, K., & Burgard, W. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980187 · Cited by 272
Self-citationAutonomous door opening and plugging in with a personal robot
Meeussen, W., Wise, M., Glaser, S., Chitta, S., Mcgann, C. P., Mihelich, P., Marder-Eppstein, E., Muja, M., Eruhimov, V., Foote, T. B., et al. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509556 · Cited by 180
Self-citationTowards Reliable Grasping and Manipulation in Household Environments
Ciocarlie, M., Hsiao, K., Jones, E. G., Chitta, S., Rusu, R. B., & Sucan, I. A. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-28572-1_17 · Cited by 158
Self-citationLaser-based perception for door and handle identification
Rusu, R. B., Meeussen, W., Chitta, S., & Beetz, M. (2009) · International Conference on Advanced Robotics (ICAR) · Cited by 69
Self-citation
Cited by
LOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Mahjourian, R., Wicke, M., & Angelova, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00594 · Cited by 775
Low-drift and real-time lidar odometry and mapping
Zhang, J., & Singh, S. (2017) · Autonomous Robots · doi:10.1007/s10514-016-9548-2 · Cited by 820
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors
Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., & Kwok, N. M. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0824-y · Cited by 555
Analysis and Observations From the First Amazon Picking Challenge
Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J. M., & Wurman, P. R. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2600527 · Cited by 425
“As for the software used, 75% (18) of the teams used the “Point Cloud Library” (PCL) [8], 67% (16) used the “Open Computer Vision Library” (OpenCV) [9], and 33% (8) report using their “own” tools (Figure 9).”
BackgroundMethod
Metadata
- Authors
- Rusu, R. B., & Cousins, S.
- Venue
- 2011 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 1-4
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2152864241
- Cited by
- 4,885
- References
- 10
- FWCI
- 439.52
- Type
- conference paper
- 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.
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: 3D is here: Point Cloud Library (PCL). https://app.cochat.ai/papers/10.1109/icra.2011.5980567
- The paper
- Rusu, R. B., & Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). 2011 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2011.5980567
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.
