Going Further with Point Pair Features

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

Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes

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

Model 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

Latent-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

Out-of-region keypoint localization for 6D pose estimation

Zhang, X., Jiang, Z., & Zhang, H. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.103854 · Cited by 13

W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose RegressionPreprint

Xu, Z., Chen, K., & Jia, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11888 · Cited by 13

Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images

Mercier, J.-P., Mitash, C., Giguère, P., & Boularias, A. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794112 · Cited by 12

Learning Multiview 3D Point Cloud RegistrationPreprint

Gojčič, Ž., Zhou, C., Wegner, J. D., Guibas, L. J., & Birdal, T. (2020) · arXiv (Cornell University) · doi:10.3929/ethz-b-000452051 · Cited by 12

HCCG: Efficient high compatibility correspondence grouping for 3D object recognition and 6D pose estimation in cluttered scenes

Wu, L., Li, X., Zhong, K., Li, Z., Wang, C., & Shi, Y. (2022) · Measurement · doi:10.1016/j.measurement.2022.111296 · Cited by 11

BOLD3D: A 3D BOLD descriptor for 6Dof pose estimation

Zhou, J., Liu, Y., Liu, J., Xie, Q., Zhang, Y., Zhu, X., & Ding, X. H. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.05.008 · Cited by 11

Accurate Pose Estimation of the Texture-Less Objects With Known CAD Models via Point Cloud Matching

Li, H., Zeng, Q., Zhuang, T., Huang, Y., & Zhang, X. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3316457 · Cited by 10

Pose estimation for workpieces in complex stacking industrial scene based on RGB images

Zhang, Y., Yi, J., Chen, Y., Dai, Z., Han, F., & Cao, S. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02857-7 · Cited by 10

Path Integral Guided Policy SearchPreprint

Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Stefan Schaal, A., & Levine, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.00529 · Cited by 10

GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

Zheng, L., Tse, T. H. E., Wang, C., Sun, Y., Chen, H., Leonardis, A., Zhang, W., & Chang, H. J. (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/cvpr52733.2024.01017 · Cited by 9

A framework for tool cognition in robots without prior tool learning or observation

Tee, K. P., Cheong, S., Li, J., & Ganesh, G. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00500-9 · Cited by 9

Pose estimation algorithm based on point pair features using PointNet + +

Chen, Y., Li, Z., Li, Q., & Zhang, M. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01508-x · Cited by 8

Holistic and local patch framework for 6D object pose estimation in RGB-D images

Zhang, H., & Cao, Q. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.01.005 · Cited by 8

Edge and Corner Detection in Unorganized Point Clouds for Robotic Pick and Place Applications

Vohra, M., Prakash, R., & Behera, L. (2021) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0010501200002994 · Cited by 8

Estimating 3D Motion and Forces of Human–Object Interactions from Internet Videos

Li, Z., Sedlář, J., Carpentier, J., Ivan Laptev, A., Mansard, N., & Šivic, J. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01540-1 · Cited by 8

A Convolutional Neural-Network-Based Training Model to Estimate Actual Distance of Persons in Continuous Images

Tsai, Y., Modales, A. V., & Lin, H.-T. (2022) · Sensors · doi:10.3390/s22155743 · Cited by 8

Fast 6D object pose refinement in depth images

Zhang, H.-R., & Cao, Q.-X. (2019) · Applied intelligence (Boston) · doi:10.1007/s10489-018-1376-y · Cited by 7

Leveraging Symmetries to Improve Object Detection and Pose Estimation from Range Data

Alexandrov, S. V., Patten, T., & Vincze, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34995-0_36 · Cited by 8

6D Pose Estimation from Point Cloud Using an Improved Point Pair Features Method

Wang, H., Wang, H., & Zhuang, C. (2021) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar52225.2021.9463502 · Cited by 7

Deep Learning based Iterative 6D Pose Estimation

Chen, P., Zheng, Z., & Dai, W. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728163 · Cited by 7

A fast 3D object recognition algorithm using plane-constrained point pair features

Xiao, Z., Gao, J., Wu, D., Zhang, L., & Chen, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09525-x · Cited by 7

Incremental Multiview Point Cloud Registration with Two-stage Candidate Retrieval

Li, S., Zhu, J., Xie, Y., & Zhu, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111705 · Cited by 6

A multilevel object pose estimation algorithm based on point cloud keypoints

Yang, H., Jia, J., & Lu, X. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04411-5 · Cited by 6

Adaptive Pose Estimation Algorithm Based on Point Pair Feature

Chen, Y., Li, Q., & Zhang, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3355923 · Cited by 6

Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Burde, V., Benbihi, A., Burget, P., & Sattler, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00745 · Cited by 6

6DoF Pose Estimation for Industrial Manipulation Based on Synthetic Data

Brucker, M., Durner, M., Márton, Z.-C., Bálint-Benczédi, F., Sundermeyer, M., & Triebel, R. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-33950-0_58 · Cited by 6

OSSID: Online Self-Supervised Instance Detection by (And For) Pose Estimation

Gu, Q. L., Okorn, B., & Held, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3145488 · Cited by 6

Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesPreprint

Sundermeyer, M., Márton, Z.-C., Durner, M., Brucker, M., & Triebel, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.01275 · Cited by 6

Pose Detection of Aerial Image Object Based on Constrained Neural Network

Zhang, H., Liu, J., & Gao, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3175871 · Cited by 4

Advancing 3D dental scanning: The use of photogrammetry with light detection and ranging for edentulous arches

Saghiri, M. A., Saghiri, A. M., Samadi, E., Vakhnovetsky, J., Kowalczyk, A., Farhadi, M., Shahid, O., Memariani, A., & Morgano, S. M. (2024) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2024.10.032 · Cited by 5

6D Object Pose Estimation with Pairwise Compatible Geometric Features

Lin, M., Murali, V., & Karaman, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561404 · Cited by 5

Selective Embedding with Gated Fusion for 6D Object Pose Estimation

Sun, S., Liu, R., Du, Q., & Sun, S. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10198-8 · Cited by 5

Global Hypothesis Generation for 6D Object Pose EstimationPreprint

Michel, F., Kirillov, A., Brachmann, E., Krull, A., Gumhold, S., Savchynskyy, B., & Rother, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02287 · Cited by 5

GraspMixer: Hybrid of Contact Surface Sampling and Grasp Feature Mixing for Grasp Synthesis

Lee, H., Toner, T., Tilbury, D. M., & Barton, K. L. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3530795 · Cited by 4

Reconstruction-based 6D pose estimation for robotic assembly

Shi, Z., Xu, K., Li, Z., Guan, B., Wang, G., & Shang, Y. (2020) · Applied Optics · doi:10.1364/ao.405444 · Cited by 4

Pose Estimation from RGB Images of Highly Symmetric Objects using a Novel Multi-Pose Loss and Differential Rendering

Bengtson, S. H., Åström, H., Moeslund, T. B., Topp, E. A., & Krueger, V. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636839 · Cited by 4

Precise geometry and pose measurement of in-hand objects with simple features using a multi-camera system

Chen, Z., & Li, T. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.07.020 · Cited by 4

An Improved 6D Pose Estimation Method Based on Point Pair Feature

Wang, G., Yang, L., & Liu, Y. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164326 · Cited by 4

Focal segmentation for robust 6D object pose estimation

Ye, Y., & Park, H. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16937-y · Cited by 3

TPSFusion: A Transformer-based pyramid screening fusion network for 6D pose estimation

Zhu, J., Li, B., & Zhao, X. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105402 · Cited by 3

Instance by Instance: An Iterative Framework for Multi-Instance 3D Registration

Yang, J., Cao, X., Zhang, X., Cheng, Y., Qi, Z., & Quan, S. (2025) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.125058 · Cited by 3

Point Pair Feature based 6D pose estimation for robotic grasping

Fu, H., Mei, X., Zhang, Z., Zhao, W., & Yang, J. (2020) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec48623.2020.9084720 · Cited by 3

End-To-End Real-Time Visual Perception Framework for Construction Automation

Vohra, M., Kumar, A., Prakash, R., & Behera, L. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658630 · Cited by 3

Robust Object Pose Estimation Based on Improved Point Pair Features Method

Ren, J., Wu, J., & Liu, Y. (2022) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec55388.2022.10019963 · Cited by 3

Efficient object recognition under cluttered scenes via descriptor-based matching and single point voting

He, X., Liu, Y., Zhou, J., Zhang, Y., & Wang, J. (2024) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2024.102394 · Cited by 3

Pose Estimation with Mismatching Region Detection in Robot Bin Picking

Wang, Z., Jia, L., Zhang, L., & Zhuang, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-65292-4_4 · Cited by 3

DCNet

Chen, Z., Yang, W., Xu, Z., Xie, X., Huang, L., & null, N. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413672 · Cited by 3

Template Matching for 3D Objects in Large Point Clouds Using DBMS

Varga, D., Szalai-Gindl, J. M., Formanek, B., Vaderna, P., Dobos, L., & Laki, S. (2021) · IEEE Access · doi:10.1109/access.2021.3082848 · Cited by 3

The Best of Both Worlds: Learning Geometry-based 6D Object Pose Estimation.Preprint

Jafari, O. H., Mustikovela, S. K., Pertsch, K., Brachmann, E., & Rother, C. (2017) · arXiv (Cornell University) · Cited by 3

P$^2$GNet: Pose-Guided Point Cloud Generating Networks for 6-DoF Object Pose EstimationPreprint

Yu, P., Rao, Y., Lu, J., & Zhou, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.09316 · Cited by 3

Pose Acquisition of Non-Cooperative Spacecraft from LiDAR: Comparison of Methods Based on Point Correspondences

Tecchia, C., Piccinin, M., Nocerino, A., Opromolla, R., & Hillenbrand, U. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-1416 · Cited by 2

Point Pair Features Based Object Recognition with Improved Training Pipeline

Zhu, Y., Zhang, X., Zhu, L., & Cai, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97589-4_30 · Cited by 2

Object Recognition and 3D Pose Estimation Using Improved VGG16 Deep Neural Network in Cluttered Scenes

He, S., Liang, G., Chen, F., Wu, X., & Feng, W. (2018) · doi:10.1145/3148453.3306266 · Cited by 2

6-D Object Pose Estimation Based on Point Pair Matching for Robotic Grasp Detection

Yu, S. W., Zhai, D., Zhan, Y., Wang, W., Guan, Y., & Xia, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3442433 · Cited by 2

Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices

Yang, X., Yu, Z., & Banerjee, A. G. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711289 · Cited by 2

Point Pair Feature Matching: Evaluating Methods to Detect Simple Shapes

Ziegler, M., Rudorfer, M., Kroischke, X., Krone, S., & Krüger, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34995-0_40 · Cited by 2

End to End Robust Point-Cloud Alignment Using Unsupervised Deep Learning

Chen, X., Chen, Y., & Najjaran, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-54407-2_14 · Cited by 2

LHFF-Net: Local heterogeneous feature fusion network for 6DoF pose estimation

Wang, F., He, Z., Zhang, X., & Jiang, Y. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01364-y · Cited by 2

GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm

Mohr, L., Geles, I., & Fraundorfer, F. (2023) · European Conference on Mobile Robots · doi:10.1109/ecmr59166.2023.10256277 · Cited by 2

3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose EstimationPreprint

Liu, Y., Zhou, J., Zhang, Y., Ding, C., & Wang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06887 · Cited by 2

Anchor-Based 6D Object Pose Estimation

Liu, Z., Wang, H., & Liu, F. (2021) · International Conference on Virtual Reality · doi:10.1109/icvr51878.2021.9483838 · Cited by 1

IRPE: Instance-level reconstruction-based 6D pose estimator

Jin, L., Zhou, G., Liu, Z., Yu, Y.-C., Zhang, T., Yang, M., & Zhou, J. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105340 · Cited by 1

Homologous multimodal fusion network with geometric constraint keypoints selection for 6D pose estimation

Guo, Y., Wang, F., & Ding, Q. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126022 · Cited by 1

Swin-AFF: an improved accuracy 6D pose estimation network for high reflection and texture-less workpieces based on Swin transformer

Li, Z., Guo, Z., Feng, Y., Zhang, G., Wang, P., & Wu, W. (2025) · The Visual Computer · doi:10.1007/s00371-025-03900-1 · Cited by 1

Recovering 6D Object Pose: Multi-modal Analyses on Challenges.

Şahin, C., & Kim, T. (2017) · Cited by 1

Estimating 3D Position of Strongly Occluded Object with Semi-Real Time by Using Auxiliary 3D Points in Occluded Space

Sumiyoshi, S., & Yoshida, Y. (2019) · International Journal of Automation Technology · doi:10.20965/ijat.2019.p0464 · Cited by 1

Sparse Estimation under Saturated Condition for 3D Measurement and Its Application for Bin-Picking Robot

Chiba, N., & Hashimoto, K. (2020) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.86.106 · Cited by 1

Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation

Chiba, N., Li, M., Imakura, A., & Hashimoto, K. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961622 · Cited by 1

A High Accuracy and Recall Rate 6D Pose Estimation Method Using Point Pair Features for Bin-picking

Deng, J., Qu, W., & Fang, S. (2022) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc55256.2022.10034147 · Cited by 1

A Pose Estimation Method Combining Instance Segmentation and Point Pair Features

Xin, Y., & Peng, H. (2023) · International Journal of Science and Engineering Applications · doi:10.7753/ijcatr1204.1010 · Cited by 1

Research on the Intelligent Recognition Method of the Shipboard Equipment Status

Li, C., Chao, J., He, N., Zuo, Y., Xie, M., & Huang, Y. (2023) · IEEE International Conference on Control, Electronics and Computer Technology (ICCECT) · doi:10.1109/iccect57938.2023.10140324 · Cited by 1

GAFAR revisited—Exploring the limits of point cloud registration on sparse subsets

Mohr, L., Geles, I., & Fraundorfer, F. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2024.104870 · Cited by 1

Fast 6D Object Pose Estimation from a RGB-D Image Using Balanced Pose Tree

Konishi, Y., Hattori, K., & Hashimoto, M. (2018) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.84.348 · Cited by 1

Learning to Predict 2D Object Instances by Applying Model-Based 6D Pose Estimation

Kisner, H., Schreiter, T., & Thomas, U. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19648-6_57 · Cited by 1

Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image

Pokale, A., Aggarwal, A., Jatavallabhula, K. M., & Krishna, K. M. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00028 · Cited by 1

6D Pose Estimation of 3D Objects in Scenes with Mutual Similarities and Occlusions

Chen, T., & Sun, L. (2019) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-030-00665-5_93

6DoF Pose Estimation for Intricately-Shaped Object with Prior Knowledge for Robotic Picking

Jiao, T., Xia, Y., Gao, X., Chen, Y., & Zhao, Q. (2019) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isass.2019.8757758

Learning Dense Correspondences for Video Objects

Chin, W.-C., Jhang, Z., Chen, H.-T., & Ito, K. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803399

Scene Regions Guided Pose Estimation Using an Improved Voting Method in Cluttered Scenes

Tian, G., & Jia, Z. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961693

Learning Dense Correspondences via Local and Non-local Feature Fusion

Chin, W.-C., Jhang, Z., Huang, Y., Ito, K., & Chen, H.-T. (2020) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Fast and Accurate Pose Estimation for Industrial Workpieces Robotic Picking

Tang, Q., Gao, X., Li, S., & Zhao, Q. (2021) · doi:10.1145/3475851.3475860

Color Point Pair Feature Light

Chipana, L. R. I., & Loaiza, M. E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90436-4_28

Assistance Robotics and Biosensors

Torres, F., Úbeda, A., & Puente, S. T. (2018) · MDPI eBooks · doi:10.3390/books978-3-03897-395-9

Hccg: Efficient High Compatibility Correspondence Grouping for 3d Object Recognition and 6d Pose Estimation in Cluttered ScenesPreprint

Wu, L., kai, Z., Xi, L., Li, Z., Wang, C., & Shi, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3998828

An Improved Point-to-Feature Recognition Algorithm for 3D Vision Detection

Li, J., Guo, Q., Gao, G., Tang, S., Min, G., Li, C., & Yu, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13822-5_18

Research on Pose Estimation Method of Medicine Box Based on Secondary Key Point Matching

Yang, X., Wang, S., Zhang, W., & Qiu, X. (2022) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2022.18951

Semantic Segmentation based network for 6D pose estimation

Wang, D., Xu, N., Lin, X., & Wang, Z. (2022) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec55388.2022.10020047

Research on key technologies of random stacking components automatically crawl

Yu, H., Yanjun, J., Zhu, X., & Seqi, J. (2022) · World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) · doi:10.1109/wcmeim56910.2022.10021397

Artificial data generation pipeline for visual grasping deep learning

Casteluci, L. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.18.2023.tde-16062023-112737

Pose Estimation Based on Point Pair Features with Optimized Voting and Verification Strategies

Chen, G., Gao, A., Liu, W., Liu, C., & Xiong, Z. (2023) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim46323.2023.10196191

Modeling and Interpreting 6-D Object Pose Estimation

Soler, D., Hirata, R., & Espadoto, M. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10223067

Pose Estimation of Sheet Stampings Based on Point Pair Features

Gao, A., Chen, G., Yan, S., Liu, J., & Xiong, Z. (2023) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar58764.2023.10249262

6D Pose Estimation Method Using Curvature-Enhanced Point-Pair Features

Liu, Y., & Feng, S. (2023) · IEEE Access · doi:10.1109/access.2023.3327787

The method of peg-in-hole assembly based on 3D vision inspection and compliant force control

Yao, X., Jin, L., Li, F., & Song, R. (2024) · International Conference on Frontiers of Intelligent Manufacturing and Automation · doi:10.1145/3704558.3705530

Performance of Image Enhancement Techniques on the 6D Pose Estimation Method under Complex Lighting Conditions

Nugroho, B. S., Puspaningrum, E. Y., & Yuniarti, A. (2025) · Nusantara Science and Technology Proceedings · doi:10.11594/nstp.2025.4755

A 2-DoF on-line Template Matching Strategy for LIDAR-based Pose Initialization of Non-Cooperative Spacecraft

Palescandolo, M., Nocerino, A., Fasano, G., Opromolla, R., & Grassi, M. (2025) · IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) · doi:10.1109/metroaerospace64938.2025.11114516

Détection et estimation de pose d'instances d'objet rigide pour la manipulation robotisée

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