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
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]
Citations
Cites
Deep Residual Learning for Image Recognition
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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
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
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
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
Cited by
Sorted by citations
Real-world robot applications of foundation models: a review
Kawaharazuka, K., Matsushima, T., Gambardella, A., Guo, J., Paxton, C., & Zeng, A. (2024) · Advanced Robotics · doi:10.1080/01691864.2024.2408593 · Cited by 117
FoundPose: Unseen Object Pose Estimation with Foundation Features
Örnek, E. P., Labbé, Y., Tekin, B., Ma, L., Keskin, C., Förster, C., & Hodaň, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_10 · Cited by 84
GDRNPP: A Geometry-Guided and Fully Learning-Based Object Pose Estimator
Liu, X., Zhang, R., Zhang, C., Wang, G., Tang, J., Li, Z., & Ji, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3553485 · Cited by 43
FreeZe: Training-Free Zero-Shot 6D Pose Estimation with Geometric and Vision Foundation Models
Caraffa, A., Boscaini, D., Hamza, A., & Poiesi, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73226-3_24 · Cited by 48
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Liu, J., Sun, W., Yang, H., Zeng, Z., Liu, C., Zheng, J., Liu, X., Rahmani, H., Sebe, N., & Mian, A. S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02646-6 · Cited by 31
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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