Going Further with Point Pair Features
Open accessIntegrity signals
Registry
Cites
Statistics
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?
L2 or L1 loss does not represent correctly the actual angular distance for any orientation representation.
“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]
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 452 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
3D Object Detection and Localization Using Multimodal Point Pair Features
Drost, B., & Ilić, S. (2012) · doi:10.1109/3dimpvt.2012.53 · Cited by 87 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Flagged first, then by citations
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 · see citing works
Rad, M., & Lepetit, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.413 · Cited by 836 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects
Hodaň, T., Haluza, P., Obdržálek, Š., Matas, J., Lourakis, M., & Zabulis, X. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.103 · Cited by 517 · see citing works
BOP: Benchmark for 6D Object Pose Estimation
Hodaň, T., Michel, F., Brachmann, E., Kehl, W., Buch, A. G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_2 · Cited by 470 · see citing works
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
Wang, G., Manhardt, F., Tombari, F., & Ji, X. (2021) · 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/cvpr46437.2021.01634 · Cited by 468 · see citing works
EPOS: Estimating 6D Pose of Objects With Symmetries
Hodaň, T., Baráth, D., & Matas, J. (2020) · 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/cvpr42600.2020.01172 · Cited by 251 · see citing works
RelocNet: Continuous Metric Learning Relocalisation Using Neural Nets
Balntas, V., Li, S., & Prisacariu, V. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_46 · Cited by 231 · see citing works
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197244 · Cited by 229 · see citing works
“In their benchmark, [26] concluded that learningbased solutions are still not on par with point-cloud-based methods [27] in terms of precision.”
BackgroundMethodDeepIM: Deep Iterative Matching for 6D Pose Estimation
Li, Y., Wang, G., Ji, X., Yu, X., & Fox, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01250-9 · Cited by 223 · see citing works
Learning Multiview 3D Point Cloud Registration
Gojčič, Ž., Zhou, C., Wegner, J. D., Guibas, L. J., & Birdal, T. (2020) · 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/cvpr42600.2020.00183 · Cited by 200 · see citing works
Georgiou, T., Liu, Y., Chen, W., & Lew, M. S. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00183-w · Cited by 194 · see citing works
Eswaran, M., Gulivindala, A. K., Inkulu, A. K., & Bahubalendruni, M. V. A. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118983 · Cited by 169 · see citing works
Lin, J., Wei, Z., Li, Z., Xu, S., Jia, K., & Li, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00354 · Cited by 162 · see citing works
Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection
Sundermeyer, M., Márton, Z.-C., Durner, M., & Triebel, R. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01243-8 · Cited by 148 · see citing works
Collective robot reinforcement learning with distributed asynchronous guided policy search
Yahya, A., Li, A., Kalakrishnan, M., Chebotar, Y., & Levine, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202141 · Cited by 135 · see citing works
Global Hypothesis Generation for 6D Object Pose Estimation
Michel, F., Kirillov, A., Brachmann, E., Krull, A., Gumhold, S., Savchynskyy, B., & Rother, C. (2017) · 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.2017.20 · Cited by 126 · see citing works
Path integral guided policy search
Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Stefan Schaal, A., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989384 · Cited by 124 · see citing works
3D Local Features for Direct Pairwise Registration
Deng, H., Birdal, T., & Ilić, S. (2019) · 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.2019.00336 · Cited by 124 · see citing works
G2L-Net: Global to Local Network for Real-Time 6D Pose Estimation With Embedding Vector Features
Chen, W., Jia, X., Chang, H. J., Duan, J., & Leonardis, A. (2020) · 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/cvpr42600.2020.00429 · Cited by 121 · see citing works
Multi-Path Learning for Object Pose Estimation Across Domains
Sundermeyer, M., Durner, M., Puang, E. Y., Márton, Z.-C., Vaškevičius, N., Arras, K. O., & Triebel, R. (2020) · 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/cvpr42600.2020.01393 · Cited by 113 · see citing works
A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators
Şahin, C., Garcia-Hernando, G., Sock, J., & Kim, T. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103898 · Cited by 114 · see citing works
6D pose estimation using an improved method based on point pair features
Vidal, J., Lin, C., & Martí, R. (2018) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2018.8384709 · Cited by 109 · see citing works
FS6D: Few-Shot 6D Pose Estimation of Novel Objects
He, Y., Wang, Y., Fan, H., Sun, J., & Chen, Q. (2022) · 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/cvpr52688.2022.00669 · Cited by 100 · see citing works
Welling, M., Sebe, N., Matas, J., & Leibe, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9 · Cited by 99 · see citing works
6D Object Pose Regression via Supervised Learning on Point Clouds
Gao, G., Lauri, M., Wang, Y., Hu, X., Zhang, J., & Frintrop, S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197461 · Cited by 95 · see citing works
HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation
Zheng, L., Wang, C., Sun, Y., Dasgupta, E., Chen, H., Leonardis, A., Zhang, W., & Chang, H. J. (2023) · 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/cvpr52729.2023.01646 · Cited by 87 · see citing works
A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning
Hoque, S., Arafat, M. Y., Xu, S., Maiti, A., & Wei, Y.-C. (2021) · IEEE Access · doi:10.1109/access.2021.3114399 · Cited by 85 · see citing works
HoPPF: A novel local surface descriptor for 3D object recognition
Zhao, H., Tang, M., & Ding, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107272 · Cited by 82 · see citing works
Estimating 3D Motion and Forces of Person-Object Interactions From Monocular Video
Li, Z., Sedlář, J., Carpentier, J., Laptev, I., Mansard, N., & Šivic, J. (2019) · 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.2019.00884 · Cited by 79 · see citing works
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
Yu, H., Hou, J., Qin, Z., Saleh, M., Shugurov, I., Wang, K., Busam, B., & Ilić, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3349199 · Cited by 78 · see citing works
Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud
Guo, J., Xing, X., Quan, W., Dong-ming Yan, A., Gu, Q., Liu, Y., & Zhang, X. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3078109 · Cited by 71 · see citing works
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
Buch, A. G., Kiforenko, L., & Kraft, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.443 · Cited by 70 · see citing works
Dong, Z.-K., Liu, S., Zhou, T., Cheng, H., Zeng, L., Yu, X., & Liu, H. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967895 · Cited by 70 · see citing works
Semantic part segmentation method based 3D object pose estimation with RGB-D images for bin-picking
Zhuang, C., Wang, Z., Zhao, H., & Ding, H. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102086 · Cited by 66 · see citing works
SilhoNet: An RGB Method for 6D Object Pose Estimation
Billings, G., & Johnson‐Roberson, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2928776 · Cited by 66 · see citing works
Rad, M., & Lepetit, V. (2017) · arXiv (Cornell University) · Cited by 61 · see citing works
Self-citationOcclusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
Wang, G., Manhardt, F., Liu, X., Ji, X., & Tombari, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3136301 · Cited by 69 · see citing works
Fast template matching and pose estimation in 3D point clouds
Vock, R., Dieckmann, A., Ochmann, S., & Klein, R. (2019) · Computers & Graphics · doi:10.1016/j.cag.2018.12.007 · Cited by 60 · see citing works
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
Jafari, O. H., Mustikovela, S. K., Pertsch, K., Brachmann, E., & Rother, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_30 · Cited by 59 · see citing works
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
Shugurov, I., Zakharov, S., & Ilić, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3118833 · Cited by 58 · see citing works
3D Object Recognition and Pose Estimation From Point Cloud Using Stably Observed Point Pair Feature
Li, D., Wang, H., Liu, N., Wang, X., & Xu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2978255 · Cited by 50 · see citing works
6D-VNet: End-To-End 6DoF Vehicle Pose Estimation From Monocular RGB Images
Wu, D., Zhuang, Z., Xiang, C., Zou, W., & Li, X. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00163 · Cited by 50 · see citing works
Zhou, J., Chen, K., Xu, L., Dou, Q., & Qin, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01284 · Cited by 49 · see citing works
Park, K., Patten, T., Prankl, J., & Vincze, M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794448 · Cited by 48 · see citing works
SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix
Xu, J., Tang, X., Yushi, Z., Sun, J., & Pu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01030 · Cited by 48 · see citing works
A Novel Depth and Color Feature Fusion Framework for 6D Object Pose Estimation
Zhou, G., Yan, Y., Wang, D., & Chen, Q. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3001533 · Cited by 46 · see citing works
A performance evaluation of point pair features
Kiforenko, L., Drost, B., Tombari, F., Krüger, N., & Buch, A. G. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.09.004 · Cited by 45 · see citing works
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
Krull, A., Brachmann, E., Nowozin, S., Michel, F., Shotton, J., & Rother, C. (2017) · 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.2017.275 · Cited by 44 · see citing works
Voting and Attention-Based Pose Relation Learning for Object Pose Estimation From 3D Point Clouds
Hoang, D.-C., Stork, J. A., & Stoyanov, T. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3189158 · Cited by 43 · see citing works
REDE: End-to-End Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination
Hua, W., Zhou, Z., Wu, J., Huang, H., Wang, Y., & Xiong, R. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062304 · Cited by 43 · see citing works
CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild
You, Y., Shi, R., Wang, W., & Lu, C. (2022) · 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/cvpr52688.2022.00674 · Cited by 43 · see citing works
PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation
Chen, W., Duan, J., Basevi, H. R. A., Chang, H. J., & Leonardis, A. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093272 · Cited by 42 · see citing works
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2019) · arXiv · Cited by 251 · see citing works
BOP: Benchmark for 6D Object Pose EstimationPreprint
Hodaň, T., Michel, F., Brachmann, E., Kehl, W., Buch, A. G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08319 · Cited by 35 · see citing works
Le, T.-T., Le, T.-S., Chen, Y., Vidal, J., & Lin, C. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103775 · Cited by 34 · see citing works
A Transformer-based multi-modal fusion network for 6D pose estimation
Hong, J.-X., Zhang, H., Liu, J., Lei, Q., Yang, L., & Du, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102227 · Cited by 32 · see citing works
Real-time 6D pose estimation from a single RGB image
Zhang, X., Jiang, Z., & Zhang, H. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.06.013 · Cited by 32 · see citing works
Havard, V., Baudry, D., Jeanne, B., Louis, A., & Savatier, X. (2021) · Virtual Reality · doi:10.1007/s10055-020-00493-z · Cited by 32 · see citing works
Neural Correspondence Field for Object Pose Estimation
Huang, L., Hodaň, T., Ma, L., Zhang, L., Tran, L., Twigg, C. D., Wu, P.-C., Yuan, J., Keskin, C., & Wang, R. Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_34 · Cited by 32 · see citing works
ZePHyR: Zero-shot Pose Hypothesis Rating
Okorn, B., Gu, Q. L., Hebert, M., & Held, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560874 · Cited by 31 · see citing works
Li, D., Liu, N., Guo, Y., Wang, X., & Xu, J. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.08.016 · Cited by 30 · see citing works
Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking
Yan, W., Xu, Z., Zhou, X., Su, Q., Li, S., & Wu, H. (2020) · IEEE Access · doi:10.1109/access.2020.2983173 · Cited by 30 · see citing works
Real-Time 6D Object Pose Estimation on CPU
Konishi, Y., Hattori, K., & Hashimoto, M. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967967 · Cited by 30 · see citing works
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
Wang, P., Manhardt, F., Minciullo, L., Garattoni, L., Meier, S., Navab, N., & Busam, B. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636856 · Cited by 29 · see citing works
Pan, P., Fan, Z.-W., Feng, B. Y., Wang, P.-H., Li, C.-X., & Wang, Z.-Y. (2023) · International Conference on 3D Vision · doi:10.1109/3dv62453.2024.00078 · Cited by 33 · see citing works
Bobkov, D., Chen, S., Jian, R., Iqbal, M. Z., & Steinbach, E. G. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2792681 · Cited by 26 · see citing works
Pipe pose estimation based on machine vision
Hu, J., Liu, S., Liu, J., Wang, Z., & Huang, H. (2021) · Measurement · doi:10.1016/j.measurement.2021.109585 · Cited by 26 · see citing works
Semi-Supervised 6D Object Pose Estimation Without Using Real Annotations
Zhou, G., Wang, D., Yan, Y., Chen, H., & Chen, Q. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3138129 · Cited by 26 · see citing works
Xu, Y., Arai, S., Tokuda, F., & Kosuge, K. (2020) · IEEE Access · doi:10.1109/access.2020.2978506 · Cited by 26 · see citing works
Coarse Alignment for Model Fitting of Point Clouds Using a Curvature-Based Descriptor
Kleppe, A. L., Tingelstad, L., & Egeland, O. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2861618 · Cited by 25 · see citing works
Instance- and Category-Level 6D Object Pose Estimation
Şahin, C., Garcia-Hernando, G., Sock, J., & Kim, T. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_11 · Cited by 25 · see citing works
A Survey of 6D Object Detection Based on 3D Models for Industrial Applications
Gorschlüter, F., Rojtberg, P., & Pöllabauer, T. (2022) · Journal of Imaging · doi:10.3390/jimaging8030053 · Cited by 24 · see citing works
Depth-Based 6DoF Object Pose Estimation Using Swin Transformer
Li, Z., & Stamos, I. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342215 · Cited by 24 · see citing works
Curve Set Feature-Based Robust and Fast Pose Estimation Algorithm
Li, M., & Hashimoto, K. (2017) · Sensors · doi:10.3390/s17081782 · Cited by 24 · see citing works
A point sampling algorithm for 3D matching of irregular geometries
Birdal, T., & Ilić, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206609 · Cited by 24 · see citing works
Detect in RGB, Optimize in Edge: Accurate 6D Pose Estimation for Texture-less Industrial Parts
Zhang, H., & Cao, Q. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794330 · Cited by 22 · see citing works
Cognitive Template-Clustering Improved LineMod for Efficient Multi-object Pose Estimation
Zhang, T., Yang, Y., Zeng, Y., & Zhao, Y. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09717-5 · Cited by 21 · see citing works
PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching
Castro, P., & Kim, T. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00229 · Cited by 21 · see citing works
Accurate Object Pose Estimation Using Depth Only
Li, M., & Hashimoto, K. (2018) · Sensors · doi:10.3390/s18041045 · Cited by 20 · see citing works
YOLO-6D+: Single Shot 6D Pose Estimation Using Privileged Silhouette Information
Kang, J., Liu, W., Tu, W., & Yang, L. (2020) · International Conference on Image Processing and Robotics (ICIPRoB) · doi:10.1109/icip48927.2020.9367354 · Cited by 20 · see citing works
FPFH Revisited: Histogram Resolutions, Improved Features, and Novel Representation
Szalai-Gindl, J. M., & Varga, D. (2024) · IEEE Access · doi:10.1109/access.2024.3400591 · Cited by 20 · see citing works
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Gao, G., Lauri, M., Zhang, J., & Frintrop, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_44 · Cited by 20 · see citing works
Physics-based scene-level reasoning for object pose estimation in clutter
Mitash, C., Boularias, A., & Bekris, K. E. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919846551 · Cited by 20 · see citing works
Scene-level Pose Estimation for Multiple Instances of Densely Packed ObjectsPreprint
Mitash, C., Wen, B., Bekris, K. E., & Boularias, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04953 · Cited by 20 · see citing works
Combined Holistic and Local Patches for Recovering 6D Object Pose
Cao, Q., & Zhang, H. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.259 · Cited by 19 · see citing works
Zhuang, C., Wang, H., & Ding, H. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102671 · Cited by 18 · see citing works
Nigam, A., Peñate-Sánchez, A., & Agapito, L. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2858446 · Cited by 18 · see citing works
Towards markerless computer assisted surgery: Application to total knee arthroplasty
Sta, S., Ogor, J., Letissier, H., Stindel, É., Hamitouche, C., & Dardenne, G. (2021) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2296 · Cited by 18 · see citing works
Fast and Robust Pose Estimation Algorithm for Bin Picking Using Point Pair Feature
Li, M., & Hashimoto, K. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545432 · Cited by 18 · see citing works
Robot End-Effector Mounted Camera Pose Optimization in Object Detection-Based Tasks
Roveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Shahid, A. A., Bucca, G., & Piga, D. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01558-0 · Cited by 17 · see citing works
Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms
Ivorra, E., Ortega, M., Catalan, J. M., Ezquerro, S., Lledó, L. D., García-Aracil, N., & Alcañíz, M. (2018) · Sensors · doi:10.3390/s18082408 · Cited by 18 · see citing works
Recovering 6D Object Pose: A Review and Multi-modal Analysis
Şahin, C., & Kim, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_2 · Cited by 16 · see citing works
Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
Jin, S., Armeni, I., Pollefeys, M., & Baráth, D. (2024) · 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/cvpr52733.2024.01969 · Cited by 15 · see citing works
6 DoF Pose Estimation of Textureless Objects from Multiple RGB Frames
Kaskman, R., Shugurov, I., Zakharov, S., & Ilić, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_41 · Cited by 15 · see citing works
Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes
Xing, X., Guo, J., Nan, L., Gu, Q., Zhang, X., & Dong-ming Yan, A. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3121721 · Cited by 15 · see citing works
A Fast Point Cloud Recognition Algorithm Based on Keypoint Pair Feature
Ge, Z., Shen, X., Gao, Q., Sun, H., Tang, X., & Cai, Q. (2022) · Sensors · doi:10.3390/s22166289 · Cited by 14 · see citing works
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
You, Y., Ye, Z., Lou, Y., Li, C., Li, Y., Ma, L., Wang, W., & Lu, C. (2022) · 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/cvpr52688.2022.00126 · Cited by 14 · see citing works
- 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 › Engineering › Computational Mechanics › 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.
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
