FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
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/
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FoundationPose demonstrates that ViTs can achieve high precision, but its multi-stage pipeline is not designed for deployment on edge devices.
“In contrast, FoundationPose [37] demonstrates that ViTs can achieve high precision, but its multi-stage pipeline is not designed for deployment on edge devices.”
From FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation (2026) · cites this paper as [37]
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A Review on Six Degrees of Freedom (6D) Pose Estimation for Robotic Applications
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Embodied AI: A Survey on the Evolution from Perceptive to Behavioral Intelligence
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Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries
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Robo-GS: A Physics Consistent Spatial-Temporal Model for Robotic Arm with Hybrid Representation
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Learning contact-rich whole-body manipulation with example-guided reinforcement learning
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A multi-feature fusion-based pose tracking method for industrial object with visual ambiguities
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Distributed Control for Collaborative Robotic Systems Using 5G Edge Computing
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DeformGS: Scene Flow in Highly Deformable Scenes for Deformable Object Manipulation
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Self-citationOne2Any: One-Reference 6D Pose Estimation for Any Object
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Autoregressive Action Sequence Learning for Robotic Manipulation
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Automatic 3D inspection method for AR-assisted assembly based on virtual-to-real registration
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A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards
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F-HOI: Toward Fine-Grained Semantic-Aligned 3D Human-Object Interactions
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G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
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SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation
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Self-citationZero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation
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ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation
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What foundation models can bring for robot learning in manipulation: A survey
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Synthetica: Large Scale Synthetic Data Generation for Robot Perception
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InstantPose: Zero-Shot Instance-Level 6D Pose Estimation From a Single View
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HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation
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MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation
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Aim My Robot: Precision Local Navigation to Any Object
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FunGrasp: Functional Grasping for Diverse Dexterous Hands
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Research on Data Collection and Posture Optimization in Dance Training Using Smart Wearable Devices
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Motion Before Action: Diffusing Object Motion as Manipulation Condition
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FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases
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Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation
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GaussianNexus: Room-Scale Real-Time AR/VR Telepresence with Gaussian Splatting
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Deep learning-based approaches for human pose estimation in interdisciplinary physics applications
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GNC-Pose: Geometry-Aware GNC-PnP for Monocular 6D Pose Estimation
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Temporally Consistent Object 6D Pose Estimation for Robot Control
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PUGS: Zero-Shot Physical Understanding with Gaussian Splatting
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Lost & Found: Tracking Changes From Egocentric Observations in 3D Dynamic Scene Graphs
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CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
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iG-6DoF: Model-Free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting
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InterTrack: Tracking Human Object Interaction Without Object Templates
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DROP: Dexterous Reorientation via Online Planning
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In-Context Learning Enables Robot Action Prediction in LLMs
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FMimic: Foundation models are fine-grained action learners from human videos
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Robotic Evaluation Framework for 6D Object Pose Estimation Accuracy
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Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors
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DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects
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Twin-DP3: View-Invariant 3D Diffusion Policy With Digital Twin
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BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation
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Machine learning for metrology in manufacturing
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Autonomous Execution of Insertion Operations in Space Assembly Tasks
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SamPose: Generalizable Model-Free 6D Object Pose Estimation via Single-View Prompt
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Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images
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UniQuadric: A SLAM Backend for Unknown Rigid Object 3-D Tracking and Light-Weight Modeling
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Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models
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A Visual Servo Human-Robot Interaction Method Based on Unidirectional Adaptive Temporal Interaction
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Geometry aware neural radiance fields for freehand ultrasound reconstruction
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Collision-Inclusive Manipulation Planning for Occluded Object Grasping via Compliant Robot Motions
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Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision
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Distributed Invariant Kalman Filter for Object-Level Multi-Robot Pose SLAM
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Pov9D: Point Cloud-Based Open-Vocabulary 9D Object Pose Estimation
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Accelerating Residual Reinforcement Learning With Uncertainty Estimation
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RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
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Digital twins for self-driving chemistry laboratories
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Enhanced RGB-D feature extraction for 6D pose estimation
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SilRef: Joint Visual Silhouette and Tactile Pose Optimization for Transparent Object Manipulation
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A variational causal inference-based method for recognizing object state changes in videos
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ZeroBot: Learning From Scratch in Minutes With Generative Real2Sim
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TRICKY 2025 HouseCat6D Object Pose Estimation Challenge with Specular and Transparent Surfaces
Li, W., Huang, J., Jung, H., Zhai, G., Ramirez, P. Z., Costanzino, A., Tosi, F., Poggi, M., Stefano, L. D., Weibel, J.-B., et al. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00349 · Cited by 2 · see citing works
Huang, Z., Li, C., Li, Y., Liu, X., Zhang, C., Zhang, R., Fu, B., Hu, X., Qu, Y., Liu, M., et al. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00253 · Cited by 2 · see citing works
- Authors
- Wen, B., Yang, W., Kautz, J., & Birchfield, S.
- Affiliations
- Nvidia (United Kingdom) (GB)
- 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)
- 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
- 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 › Engineering › Computational Mechanics › 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 ›
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