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

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

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

Rad, M., & Lepetit, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.413 · Cited by 836

PPFNet: 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

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

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

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 493

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

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 238

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

DeepIM: Deep Iterative Matching for 6D Pose Estimation

Li, Y., Wang, G., Ji, X.-Y., Xiang, Y., & Fox, D. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-019-01250-9 · Cited by 821

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

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

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 206

Augmented reality-based guidance in product assembly and maintenance/repair perspective: A state of the art review on challenges and opportunities

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 179

DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency

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

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

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 143

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

Path integral guided policy search

Chebotar, Y., Kalakrishnan, M., Yahya, A., Li, A., Schaal, S., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989384 · Cited by 131

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

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 126

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

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 121

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

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 110

Computer Vision – ECCV 2016

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

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

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

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 91

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

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 81

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

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

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

PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios

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

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

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

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using DepthPreprint

Rad, M., & Lepetit, V. (2017) · arXiv (Cornell University) · Cited by 61

Occlusion-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

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

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

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 60

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

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

Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation

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 53

Multi-Task Template Matching for Object Detection, Segmentation and Pose Estimation Using Depth Images

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

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

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

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

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

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

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

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

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 45

Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering

Proenca, P. F., & Gao, Y. (2019) · arXiv · Cited by 251

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

6D pose estimation with combined deep learning and 3D vision techniques for a fast and accurate object grasping

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

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

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

A use case study comparing augmented reality (AR) and electronic document-based maintenance instructions considering tasks complexity and operator competency level

Havard, V., Baudry, D., Jeanne, B., Louis, A., & Savatier, X. (2021) · Virtual Reality · doi:10.1007/s10055-020-00493-z · Cited by 32

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

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

3D object recognition and pose estimation for random bin-picking using Partition Viewpoint Feature Histograms

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

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

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

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/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros51168.2021.9636856 · Cited by 41

Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images

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

Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor

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

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

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

A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color

Xu, Y., Arai, S., Tokuda, F., & Kosuge, K. (2020) · IEEE Access · doi:10.1109/access.2020.2978506 · Cited by 26

Coarse Alignment for Model Fitting of Point Clouds Using a Curvature-Based Descriptor

Kleppe, A., Tingelstad, L., & Egeland, O. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2861618 · Cited by 22

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 27

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 23

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

Curve Set Feature-Based Robust and Fast Pose Estimation Algorithm

Li, M., & Hashimoto, K. (2017) · Sensors · doi:10.3390/s17081782 · Cited by 24

A point sampling algorithm for 3D matching of irregular geometries

Birdal, T., & Ilic, S. (2017) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros.2017.8206609 · Cited by 22

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

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

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

Accurate Object Pose Estimation Using Depth Only

Li, M., & Hashimoto, K. (2018) · Sensors · doi:10.3390/s18041045 · Cited by 20

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

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

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

Physics-based scene-level reasoning for object pose estimation in clutter

Mitash, C., Boularias, A., & Bekris, K. E. (2018) · Int. J. Robotics Res. · doi:10.1177/0278364919846551 · Cited by 22

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

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

AttentionVote: A coarse-to-fine voting network of anchor-free 6D pose estimation on point cloud for robotic bin-picking application

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Detect Globally, Label Locally: Learning Accurate 6-DOF Object Pose Estimation by Joint Segmentation and Coordinate Regression

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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

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

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

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

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

Multiway Point Cloud Mosaicking with Diffusion and Global Optimization

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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 16

Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes

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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

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

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

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

Point Pair Feature Based Object Detection for Random Bin Picking

Abbeloos, W., & Goedemé, T. (2016) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2016.59 · Cited by 24

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

Efficient feature learning for point cloud-based 3D object detection

He, C. (n.d.) · PolyU Institutional Research Archive (Hong Kong Polytechnic University)