Methods › Computer Vision › 6D Pose Estimation Models › FFB6D
FFB6D
Introduced by Yisheng He et al. in FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
FFB6D is a full flow bidirectional fusion network for 6D pose estimation of known objects from a single RGBD image. Unlike previous works that extract the RGB and point cloud features independently and fuse them in the final stage, FFB6D builds bidirectional fusion modules as communication bridges in the full flow of the two networks. In this way, the two networks can obtain complementary information from the other and learn representations containing rich appearance and geometry information of the scene.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation 3 Mar 2021 · 3 repositories · arXiv:2103.02242Syntology ran 1 of 8 samples · 7 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| 6D Pose Estimation | 1 |
| Pose Estimation | 1 |
| Representation Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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