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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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“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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Metadata
- 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 › 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
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