The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Going Further with Point Pair Features
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“While several works [13, 16, 24] have used L 2 or L 1 loss to regress orientation. This does not represent correctly the actual angular distance for any orientation representation.”
From Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering (2020) · cites this paper as [24]
Citations
Cites
Fast Point Feature Histograms (FPFH) for 3D registration
Rusu, R. B., Blodow, N., & Beetz, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152473 · Cited by 3,977
Using spin images for efficient object recognition in cluttered 3D scenes
Johnson, A., & Hebert, M. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.765655 · Cited by 2,656
Unique Signatures of Histograms for Local Surface Description
Tombari, F., Salti, S., & Di Stefano, L. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15558-1_26 · Cited by 1,480
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., & Navab, N. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37331-2_42 · Cited by 1,161
Self-citationModel globally, match locally: Efficient and robust 3D object recognition
Drost, B., Ulrich, M., Navab, N., & Ilić, S. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540108 · Cited by 980
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects
Salas-Moreno, R. F., Newcombe, R. A., Strasdat, H., Kelly, P. H. J., & Davison, A. J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.178 · Cited by 945
Learning 6D Object Pose Estimation Using 3D Object Coordinates
Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., & Rother, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_35 · Cited by 784
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
Mian, A. S., Bennamoun, M., & Owens, R. A. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.213 · Cited by 503
Learning descriptors for object recognition and 3D pose estimation
Wohlhart, P., & Lepetit, V. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298930 · Cited by 458
Self-citationLatent-Class Hough Forests for 3D Object Detection and Pose Estimation
Tejani, A., Tang, D., Kouskouridas, R., & Kim, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_30 · Cited by 279
Research works printed but not linked: 1
- Holz, D., Nieuwenhuisen, M., Droeschel, D., Stuckler, J., Berner, A., Li, J., Klein, R., Behnke, S.: Active recognition and manipulation for mobile robot bin picking. In: Röhrbein, F., Veiga, G., Natale, C. (eds.) Gearing Up and Accelerating Cross-fertilization Between Academic and Industrial Robotics Research in Europe. Springer, Berlin (2014)
Cited by
Sorted by citations
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Xiang, Y., Schmidt, T., Narayanan, V., & Fox, D. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.019 · Cited by 2,139
Rad, M., & Lepetit, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.413 · Cited by 836
Self-citationPPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Deng, H., Birdal, T., & Ilić, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00028 · Cited by 631
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Li, Y., Wang, G., Ji, X., Xiang, Y., & Fox, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_42 · Cited by 576
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., & Triebel, R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_43 · Cited by 564
Metadata
- Authors
- Hinterstoisser, S., Lepetit, V., Rajkumar, N., & Konolige, K.
- Affiliations
- Google (United States) (US)
- Graz University of Technology (AT)
- Corresponding author
- Hinterstoisser, Stefan
- ORCID
- Lepetit, Vincent
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 834-848
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2520352517
- Cited by
- 201
- References
- 25
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 17 · 2023: 17 · 2022: 21 · 2021: 29 · 2020: 31 · 2019: 38 · 2018: 19 · 2017: 15 · 2016: 3
- FWCI
- 45.61
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- point pair features, 3D object detection, point clouds, sensor noise, background clutter, sampling plans, voting scheme, robustness
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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