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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“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
Robotic roommates making pancakes
Beetz, M., Klank, U., Kresse, I., Maldonado, A., Mösenlechner, L., Pangercic, D., Rühr, T., & Tenorth, M. (2011) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2011.6100855 · Cited by 220
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Krull, A., Brachmann, E., Michel, F., Yang, M. Y., Gumhold, S., & Rother, C. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.115 · Cited by 181
Voting-based pose estimation for robotic assembly using a 3D sensor
Choi, C., Taguchi, Y., Tuzel, O., Liu, M., & Ramalingam, S. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225371 · Cited by 180
Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach
Rìos-Cabrera, R., & Tuytelaars, T. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.256 · Cited by 169
Point Pair Features Based Object Detection and Pose Estimation Revisited
Birdal, T., & Ilić, S. (2015) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2015.65 · Cited by 123
3D pose estimation of daily objects using an RGB-D camera
Choi, C., & Christensen, H. I. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6386067 · Cited by 114
Mobile bin picking with an anthropomorphic service robot
Nieuwenhuisen, M., Droeschel, D., Holz, D., Stückler, J., Berner, A., Li, J., Klein, R., & Behnke, S. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630892 · Cited by 102
3D Object Detection and Localization Using Multimodal Point Pair Features
Drost, B., & Ilić, S. (2012) · doi:10.1109/3dimpvt.2012.53 · Cited by 87
AUTOMATIC CORRESPONDENCE FOR 3D MODELING: AN EXTENSIVE REVIEW
Mian, A. S., Bennamoun, M., & Owens, R. A. (2005) · International Journal of Shape Modeling · doi:10.1142/s0218654305000797 · Cited by 76
Generalized 4-Points Congruent Sets for 3D Registration
Mohamad, M., Rappaport, D. D., & Greenspan, M. (2014) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2014.21 · Cited by 53
Tuzel, O., Liu, M., Taguchi, Y., & Raghunathan, A. U. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_34 · Cited by 33
Robust Instance Recognition in Presence of Occlusion and Clutter
Bonde, U., Badrinarayanan, V., & Cipolla, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_34 · Cited by 31
3D object recognition in range images using visibility context
Kim, E. Y., & Medioni, G. G. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6048203 · Cited by 14
Projective ICP and Stabilizing Architectural Augmented Reality Overlays
Fisher, R. B. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0337-0_7 · Cited by 10
Fast 3D Object Recognition of Rotationally Symmetric Objects
de Figueiredo, R. P., Moreno, P., & Bernardino, A. J. M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38628-2_14 · Cited by 6
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
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