Cited by 3573D Shape Modeling and AnalysisRobot Manipulation and LearningAdvanced Neural Network Applications

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

Wen, Bowen, Yang, Wei, Kautz, Jan, Birchfield, Stan·2024·2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr52733.2024.01692

Open this paper in CoChat — free account

Abstract

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without finetuning, as long as its CAD model is given, or a small number of reference images are captured. Thanks to the unified framework, the downstream pose estimation modules are the same in both setups, with a neural implicit representation used for efficient novel view synthesis when no CAD model is available. Strong generalizability is achieved via large-scale synthetic training, aided by a large language model (LLM), a novel transformer-based architecture, and contrastive learning formulation. Extensive evaluation on multiple public datasets involving challenging scenarios and objects indicate our unified approach outperforms existing methods specialized for each task by a large margin. In addition, it even achieves comparable results to instance-level methods despite the reduced assumptions. Project page: https://nvlabs.github.io/FoundationPose/

Integrity signals

Registry

Crossref · 10.1109/cvpr52733.2024.01692

Cites

77 of 77 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
MizAR 60 for Mizar 50
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Mask R-CNN
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 20,302
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712
NeRF: representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2022) · Communications of the ACM · doi:10.1145/3503250 · Cited by 6,852
Instant neural graphics primitives with a multiresolution hash encoding
Müller, T., Evans, A., Schied, C., & Keller, A. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530127 · Cited by 6,584
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286

Show all 73 references

Research works printed but not linked: 2

Dosovitskiy, An image is worth 16×16 words: Transformers for image recognition at scale
Hodan, BOP: Benchmark for 6D object pose estimation, 19

Sorted by citations

Items 101–200 of 357

AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generation
Zou, Y., Qi, Z., Liu, Y., Liu, W., Xu, Z., Sun, W., Li, X. R., Yang, J., & Zhang, Y. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3671320 · Cited by 2
EasyVis2: a real-time multi-view 3D visualization system for laparoscopic surgery training enhanced by a deep neural network YOLOv8-pose
Sun, Y.-H., Shen, G., Fernandes, J., Chen, J., Shada, A. L., Heise, C. P., Jiang, H., & Hu, Y. H. (2026) · Updates in Surgery · doi:10.1007/s13304-026-02687-7 · Cited by 3
Sensing the Action: Rethinking Sensor Modalities and Multi-Modal Fusion in Vision–Language–Action Models for Robotic Manipulation
Ko, B. C. (2026) · Sensors · doi:10.3390/s26113541 · Cited by 3
HERMES: Human-to-Robot Embodied Learning From Multisource Motion Data for Mobile Dexterous Manipulation
Yuan, Z., Wei, T., Gu, L., Hua, P., Liang, T., Chen, Y., & Xu, H. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3721670 · Cited by 2
On Robust Assembly of Flexible Flat Cables Combining CAD and Image Based Multiview Pose Estimation and a Multimodal Robotic Gripper
Liang, J., Buzzatto, J., Busby, B., Jiang, H., Matsunaga, S., Haraguchi, R., Mariyama, T., MacDonald, B. A., & Liarokapis, M. V. (2024) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2024.3467171 · Cited by 2
Simultaneous Geometry and Pose Estimation of Held Objects Via 3D Foundation Models
Zhi, W., Tang, H., Zhang, T., & Johnson‐Roberson, M. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3501677 · Cited by 5
Cross-Spatial Fusion and Dynamic-Range Particle Filter-Based FPGA-GPU Architecture for 1-ms RGB-Based Object Pose Tracking
Wang, W., Li, Y., Hu, T., Fuchikami, R., & Ikenaga, T. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551827 · Cited by 2
Automatic Virtual‐to‐real Calibration and Dynamic Registration of Deformable Tissue for Endoscopic Submucosal Dissection
Wang, Y., Gao, H., Wang, A., & Ren, H. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400845 · Cited by 2
Mtevent: a Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection
Awasthi, S., Gouda, A., Franke, S., Rutinowski, J., Frank Hoffmann, B., & Roidl, M. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00501 · Cited by 2
Elastic Motion Policy: An Adaptive Dynamical System for Robust and Efficient One-Shot Imitation Learning
Li, T., Sun, S., Aditya, S. S., & Figueroa, N. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246142 · Cited by 2
HGDiffuser: Efficient Task-Oriented Grasp Generation via Human-Guided Grasp Diffusion Models
Huang, D., Dong, W., Tang, C., & Zhang, H. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247551 · Cited by 5
Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning Models
Yan, J., Talegaonkar, C., Antipa, N., Terrill, E., & Merrifield, S. T. (2024) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans55160.2024.10754044 · Cited by 1
DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features
Wu, J., Li, S., Ji, S., Yang, Y., Wang, Y., Xiong, R., & Liao, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3511425 · Cited by 1
6D Pose Estimation of Unseen Objects for Industrial Augmented Reality
Durchon, H., Preda, M., & Zaharia, T. B. (2024) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp63557.2024.10792989 · Cited by 1
Six-Dimensional Pose Estimation of Molecular Sieve Drying Package Based on Red Green Blue–Depth Camera
Chen, Y., Cao, S., Wang, Q., Xu, Z., Song, T., & Jiang, Q. F. (2025) · Sensors · doi:10.3390/s25020462 · Cited by 1
GRPoseNet: a generalizable and robust 6D object pose estimation network using sparse RGB views
Shi, W., Gai, S., Da, F., Cai, Z., & Wang, J. (2025) · The Visual Computer · doi:10.1007/s00371-025-03852-6 · Cited by 1
Leveraging Multiscale Features and Dynamic Graph Convolution for Robust 6D Pose Estimation
Zhang, H., Wang, H., Liu, W., Guo, K., Lyu, W., & Zhang, J. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3566827 · Cited by 1
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios
Meng, X., Yang, J., Chen, M., Yan, C., Shi, Y., Ding, W., & Kneip, L. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3597857 · Cited by 1
UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References
Li, M., Yang, X., Wang, F.-E., Basak, H., Sun, Y., Gayaka, S., Sun, M., & Kuo, C.-H. (2025) · 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/cvpr52734.2025.00118 · Cited by 1
Multi-Keypoint Affordance Representation for Functional Dexterous Grasping
Yang, F., Luo, D., Chen, W., Lin, J., Cai, J., Yang, K., Li, Z., & Wang, Y. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3601041 · Cited by 1
Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, With Single Reference
Gao, Y., Luo, Y., Wang, J., Jia, K., & Xia, G.-S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3600413 · Cited by 1
SurgPose: Generalisable Surgical Instrument Pose Estimation Using Zero-Shot Learning and Stereo Vision
Rai, U., Xu, H., & Giannarou, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128521 · Cited by 1
Automated Planning Domain Inference for Task and Motion Planning
Huang, J., Tao, A., Marco, R., Bogdanović, M., Kelly, J. W., & Shkurti, F. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127817 · Cited by 1
High-Precision Object Pose Estimation Using Visual-Tactile Information for Dynamic Interactions in Robotic Grasping
Peng, Z., Cui, T., Chen, G., Lu, H., Yang, Y. M., & Yue, Y. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128649 · Cited by 1
ZeroBP: Learning Position-Aware Correspondence for Zero-Shot 6D Pose Estimation in Bin-Picking
Chen, J., Zhou, Z., Li, X., Zheng, Y., Bao, T., & He, Z. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128347 · Cited by 3
Neural Dynamics Augmented Diffusion Policy
Wu, R., Chen, H., Zhang, M., Lu, H., Li, Y., & Li, Y. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128651 · Cited by 4
Enhancing Imitation Learning for Robotic Assembly with 6D Pose Estimation and Reinforcement Learning Fine-Tuning
Zhang, X., & Li, Y. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137342 · Cited by 1
Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity
Venkatesh, S., Bianchini, B., Aydinoglu, A., Yang, W., & Posa, M. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3615030 · Cited by 1
Self-citation
HOCOpt: Hand–Object Contact Optimization to Improve Pose Estimation in Physical Interactions
Cao, X., Lu, T., Yu, W., Zhang, W., Cai, Y., & Wang, S. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3609053 · Cited by 1
ISyHand: A Dexterous Multi-Finger Robot Hand with an Articulated Palm
Richardson, B. A., Grüninger, F., Mack, L., Stueckler, J., & Kuchenbecker, K. J. (2025) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids65713.2025.11203015 · Cited by 1
PCHands: PCA-Based Hand Pose Synergy Representation on Manipulators with $N$-DoF
Puang, E. Y., Ceola, F., Pasquale, G., & Natale, L. (2025) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids65713.2025.11203193 · Cited by 1
FreePose: Zero-Shot 6D Object Pose Estimation Using Pretrained Foundation Models
Alsumeri, A., Zhai, D., & Xia, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3627799 · Cited by 1
LensDFF: Language-enhanced Sparse Feature Distillation for Efficient Few-Shot Dexterous Manipulation
Feng, Q., Knoll, A., Lema, D. S. M., Chen, Z., & Feng, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247703 · Cited by 1
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Zhang, M., Li, X., Xu, J., Zhou, K., Bae, H., Shen, Y. F., Xiong, C., & Dong, H. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247370 · Cited by 1
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Joyce, E. C., Zhao, Q., Burgdorfer, N., Wang, L., & Mordohai, P. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247333 · Cited by 2
6-DoF Object Tracking with Event-based Optical Flow and Frames
Li, Z., Glover, A., Bartolozzi, C., & Natale, L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245865 · Cited by 1
Learning Adaptive Dexterous Grasping from Single Demonstrations
Shi, L., Liu, Y., Zeng, L., Ai, B., Hong, Z., & Su, H. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246731 · Cited by 1
UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations
Yuan, T., Guan, B., Ye, W., Tian, Z., Yang, Y., Zhou, W., Li, Z., Huang, Y., Wang, P., Zhao, C., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37432-5_29 · Cited by 2
Comparing Semi-Autonomous Strategies for Virtual Reality Based Remote Robotic Telemanipulation: On Peg-In-Hole Tasks
Duan, S., Pace, F. D., & Liarokapis, M. V. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3653274 · Cited by 1
Learning From Videos Through Graph-to-Graphs Generative Modeling for Robotic Manipulation
Chen, G., Wang, M., Cui, T., Yang, C., Hu, M., Lu, H., Peng, Z., Zhou, T., Jiang, X., Yang, Y., et al. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3658211 · Cited by 1
Active 6D pose estimation for textureless objects using multi-view RGB frames
Yang, J., Xue, W.-J., Ghavidel, S., & Waslander, S. L. (2025) · The international journal of robotics research · doi:10.1177/02783649251411922 · Cited by 8
RelPose-TTA: Energy-based relative pose correction for test-time adaptation of category-level object pose estimation
Zhan, Y., Wang, X., Liu, Z., Lian, S., & Yang, T. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.105928 · Cited by 1
Vision-Based Pallet Detection and Load Height Estimation for Autonomous Material Handling Systems
Jathe, N., Staar, B., Lütjen, M., & Freitag, M. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.01.018 · Cited by 1
DemoHLM: From One Demonstration to Generalizable Humanoid Loco-Manipulation
Fu, Y., Xie, F., Xu, C., Xiong, J., Yuan, H., & Lu, Z. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3666395 · Cited by 16
MR6D: Benchmarking 6D Pose Estimation for Mobile Robots
Gouda, A., Awasthi, S., Blesing, C., Manohar, L., Frank Hoffmann, B., & Kirchheim, A. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00255 · Cited by 1
Onto-LLM-TAMP: Knowledge-oriented Task and Motion Planning using Large Language Models
Din, M. U., Rosell, J., Akram, W., Zaplana, I., Roa, M. A., & Hussain, I. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105404 · Cited by 1
Enhancing stability and reliability in LLM-driven robotic manipulation through human skill demonstration and visual tracking
Guo, Y., Liu, G., Zhu, J., Chu, H., Wen, S., & Wang, F. (2026) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2026.100305 · Cited by 1
YOTO++: Learning Long-Horizon Closed-Loop Bimanual Manipulation From One-Shot Human Video Demonstrations
Zhou, H., Wang, R., Tai, Y., Deng, Y., Liu, G., & Jia, K. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3688078 · Cited by 1
SDT-6D: Fully Sparse Depth-Transformer for Staged End-to-End 6D Pose Estimation in Industrial Multi-View Bin Picking
Leuze, N., Hoh, M., Doğan, S., Peña, N. R., & Schoettl, A. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00811 · Cited by 1
A Model-Based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers
Zuo, K., Yang, S., & Chua, Z. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3692080 · Cited by 1
High-precision interaction in augmented reality applications using tool-mounted cameras
Conejero, V. Q., Santana, F. D. L. S., & Garrido-Jurado, S. (2026) · Virtual Reality · doi:10.1007/s10055-026-01376-5 · Cited by 1
One Video, One World: Turning Monocular Video into Physical 4D Scenes
Chen, J., Zhang, B., Chen, M., Zhang, H., Zhang, S., Zhu, C., Zhao, H., Huang, R., Li, Z., & Wang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36839-3_16 · Cited by 1
Foundation Models for Autonomous Robots in Unstructured Environments: Current State-of-the-Art, Challenges, and Future Pathways
Naderi, H., Shojaei, A., & Huang, L. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7070358 · Cited by 18
RGB-D vision for relative motion monitoring of mated components during OWT installation
Luy, H. V. J., & Ren, Z. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114729 · Cited by 1
Real-Time Temporally Consistent Monocular 6D UAV Pose Estimation for Onboard Aerial Perception
Qaderi, M. A., Hayajneh, M., Ghazo, A. T. A., & Ryalat, M. (2026) · Robotics · doi:10.3390/robotics15080145 · Cited by 1
DGS-Net: Learning Dexterous Grasps From Single-View Point Clouds via a Multi-Object Scene Dataset
Geng, T., Yang, D., Liu, Z., Zhang, L., Qi, L., Li, W., Ren, Y., Luo, S., & Ni, F. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3739901 · Cited by 1
Object Pose Transformer: Unifying Unseen Object Pose Estimation
Li, W., Garattoni, L., Despinoy, F., Navab, N., & Busam, B. (2026) · doi:10.1109/cvprf71909.2026.00046 · Cited by 1
Phys-Field: Physics-Enhanced Neural Surfaces for Sim(3) Estimation in Robotic Assembly Using a Single RGB Sensor
Wang, X., Sun, F., Wang, L., Sun, K., Zhang, X., Wang, X., He, B., & Zhou, H. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3531647 · Cited by 1
自适应融合多模态特征的6D物体位姿估计方法
Chuanfang, 臧. Z., Jianwu, 党. D., & Jiu, 雍. Y. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop241238 · Cited by 1
Marker‐based adaptive virtual military training system for enhanced immersion and realism
Tong, H., Kim, S., Yoon, H., Kim, J. J., Lee, S., Park, S., Vladimirov, B., & Lee, S. (2025) · ETRI Journal · doi:10.4218/etrij.2024-0456 · Cited by 3
The Role of Tactile Sensing for Learning Reach and Grasp
Zhang, B., Andrussow, I., Zell, A., & Martius, G. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127409 · Cited by 1
Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach
Wu, T., Zhang, J., Liang, S., Han, Z., & Dong, H. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128327 · Cited by 1
KARL: Kalman-Filter Assisted Reinforcement Learner for Dynamic Object Tracking and Grasping
Boyalakuntla, K., Boularias, A., & Yu, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245879 · Cited by 1
Learning From Planned Data to Improve Robotic Pick-and-Place Planning Efficiency
Qin, L., Wan, W., Takahashi, J., Negishi, R., Matsushita, M., & Harada, K. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3645512 · Cited by 1
FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation
Ancey, P., Price, A., Javed, S., & Salzmann, M. (2026) · 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv61042.2026.00760
“In contrast, FoundationPose [37] demonstrates that ViTs can achieve high precision, but its multi-stage pipeline is not designed for deployment on edge devices.”
Towards Embodiment Scaling Laws in Robot Locomotion
Ai, B., Dai, L., Bohlinger, N., Li, D., Mu, T., Wu, Z., Fay, K., Christensen, H. I., Peters, J., & Su, H. (2025) · arXiv · doi:10.48550/arxiv.2505.05753
Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning
Yang, Z., Liu, C., Zhou, Y., Lv, R., Cui, T., Yi, S., Zhu, H., Lu, I., & Sun, J. (2026) · arXiv · doi:10.48550/arxiv.2609.24093 · Cited by 2
“A perception stack (YOLOv5, MobileSAM, FoundationPose (Wen et al., 2024)) provides real‑time object‑pose feedback.”
Adversarial imitation learning-based network for category-level 6D object pose estimation
Sun, S., Bao, X., & Kaushik, A. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01592-6
Occluded object 6D pose estimation using foreground probability compensation
Ren, M., Jia, J., & Lu, X. (2024) · IET Computer Vision · doi:10.1049/cvi2.12314
DMG6D: A Depth-based Multi-Flow Global Feature Fusion Network for 6D Pose Estimation
Wang, Z., Zhang, Q., Sun, X., Zhu, J., & Wei, H. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711760
Zero-Shot 6D Pose Tracking for Sequential Robot Manipulation Using SAM and Online Segmentation Models
Won, S., Lee, H., & Yang, G. (2024) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas63016.2024.10773021
YuMi-Chess: Applying Synthetic Image Generation in Real World Scenarios
Metzler, J., Seiler, P. P., Haas, T., & Reichelt, D. (2024) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme62383.2024.10796873
Advanced Post-processing for Object Detection Dataset Generation
Pöllabauer, T., Berkei, S., Knauthe, V., & Kuijper, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77392-1_1
Generalizing Neural Radiance Fields for Robust 6D Pose Estimation of Unseen Appearances
Pöllabauer, T., Wirth, T., Weitz, P., Knauthe, V., Kuijper, A., & Fellner, D. W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77392-1_23
Improving Zero-Shot Template-Based 6D Pose Estimation with Geometric Features
Pöllabauer, T., Weyel, J., Knauthe, V., Berkei, S., & Kuijper, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77392-1_4
Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
Lv, Z., Guo, Y., Yang, S., Du, L., Gao, R. C., Sun, J., Han, J., Zhang, H., & Wang, Q. (2025) · Algorithms · doi:10.3390/a18040207
Multi-Level Features Fusion for Zero-Shot Object Pose Estimation
Huang, N., & Xu, C. (2025) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit63637.2025.10965269
TTAPose: Test-Time Adaptation for Unseen Object Pose Estimation
Huang, J., Yu, P. K., Navab, N., & Busam, B. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3564218
Efficient 6D Object Pose Estimation for Robotic Grasping Using Lightweight Neural Network Architectures
Grajeda, A., Castro, D., & Masood, J. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-89471-8_45
Fähigkeitsbasierte Programmierung von Industrierobotern
Belke, M., Groß, T., Petrović, O., & Brecher, C. (2025) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2025-1049
Object Pose Estimation Using Implicit Representation for Transparent Objects
Burde, V., Moroz, A., Zeman, V., & Burget, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91569-7_15
MG6D: A Deep Fusion Approach for 6D Pose Estimation With Mamba and Graph Convolution Network
Zhu, J., Li, B., & Zhao, X. (2025) · IEEE Access · doi:10.1109/access.2025.3575778 · Cited by 1
Tracking and Control of Multiple Objects During Nonprehensile Manipulation in Clutter
Xu, Z., Papallas, R., Modisett, J., Billeter, M., & Doğar, M. R. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3577437
Dexterous grasp data augmentation based on grasp synthesis with fingertip workspace cloud and contact-aware sampling
Wu, L., Jia, H., Kawaharazuka, K., Ishida, H., & Okada, K. (2025) · Advanced Robotics · doi:10.1080/01691864.2025.2524553
Autonomous Behavior Control for Quadruped Robots with Arms Based on MultiModal Large Language Model
Xiao, S., Shi, X., Chen, T., Zhang, Y., Rong, X., & Meng, J. (2025) · International Conference on Computer, Control and Robotics (ICCCR) · doi:10.1109/icccr65461.2025.11072587
FusePose: A Monocular Camera Position Estimation Network Based on Multi-Information Fusion
Wang, J., Liu, G., Wang, Y., Li, Y., & Song, W. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3589463
Poly-Autoregressive Prediction for Modeling Interactions
Thakkar, N., Sadjadpour, T., Rajasegeran, J., Ginosar, S., & Malik, J. (2025) · 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/cvpr52734.2025.01157
Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects in Bin-Picking With a Low-Cost Camera
Ni, P., Chew, C., Ang, M. H., & Chirikjian, G. S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3600139
Enhanced Probabilistic Collision Detection for Motion Planning Under Sensing Uncertainty
Wang, X., Ruan, S., Meng, X., & Chirikjian, G. S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3604700
The First WARA Robotics Mobile Manipulation Challenge - Lessons Learned
Domínguez, D. C., Iannotta, M., Kashyap, A., Sun, S., Yang, Y., Cella, C., Colombo, M., Pelosi, M., Preziosa, G. F., Tafuro, A., et al. (2025) · European Conference on Mobile Robots · doi:10.1109/ecmr65884.2025.11163319
6D Pose Tracking for Adaptive AR-Mediated Human-Robot Collaboration
Ajikumar, A., Wen, B., & Moghaddam, M. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163977
Self-citation
DRIM: Depth Restoration With Interference Mitigation in Multiple LiDAR Depth Cameras
Shin, S., Jang, J., Park, S., & Hwang, H. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3619771
Lang2Pose: Language-Based Visual Servoing and Pose Control for Autonomous Pick-and-Place in Real and Simulated Environments
Oh, H., Jeong, H., Shin, S., & Kim, J. (2025) · IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom) · doi:10.1109/metacom65502.2025.00025
PartPose: Attentive 6D Pose Estimation by Focusing on Graspable Parts of Multi-Part Deformable Objects
Okumura, R., & Taniguchi, T. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3627067
Generalizable Interaction Recognition for Learning from Demonstration Using Wrist and Object Trajectories
Pyaraka, J. C., Isaksson, M., McCormick, J., Sutjipto, S., & Sukkar, F. (2025) · Electronics · doi:10.3390/electronics14214297
Pose Detection: Integrating Machine Learning with Large Vision Models
Ashraf, M. S., Akuthota, V., Paul, T., Dass, A., Saha, S., Islam, M. S., Chowdhury, A. E., Anwar, A. S., & Roy, P. (2025) · International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) · doi:10.1109/iacis65746.2025.11211028
Kalib: Easy Hand-Eye Calibration with Reference Point Tracking
Tang, T., Liu, M., Xu, W., & Lu, C. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247188
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning
Gao, G. J., Li, T., & Figueroa, N. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246616 · Cited by 4
Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies
Liu, Y., Zhang, X., Chang, H., & Boularias, A. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247676
PACR: Point-Axis Constraint Reasoning for Enhanced Robotic Manipulation with Dexterity and Compliance
Xiong, H., Mu, Y., Liu, Z., Fan, Y., Huang, Y., & Liu, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246460

Authors: Wen, B., Yang, W., Kautz, J., & Birchfield, S.
Affiliations: Nvidia (United Kingdom) (GB)
ORCID: Wen, Bowen
Yang, Wei
Kautz, Jan
Birchfield, Stan
Venue: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2024-06-16
Pages: 17868-17879
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr52733.2024.01692
OpenAlex: W4402754209
Cited by: 353
References: 73
Citations by year: 2026: 165 · 2025: 161 · 2024: 27
FWCI: 98.24
Type: conference paper
Language: en
Open access: closed
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: 6D pose estimation, foundation models, model-based pose estimation, CAD models, implicit neural representations, novel view synthesis, large language models, contrastive learning, transformer models

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

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks