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]
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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
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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