Methods › Computer Vision › 6D Pose Estimation Models › PixLoc
PixLoc
Introduced by Paul-Edouard Sarlin et al. in Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
PixLoc is a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. It is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. As the CNN never sees 3D points, PixLoc can generalize to any 3D structure available. This includes sparse SfM point clouds, dense depth maps from stereo or RGBD sensors, meshes, Lidar scans, but also lines and other primitives.
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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Back to the Feature: Learning Robust Camera Localization from Pixels to Pose 16 Mar 2021 · 2 repositories · arXiv:2103.09213Syntology ran 7 of 10 samples · 3 unverified · 10 pointer-only (licence)
Tasks archive 2025-07-28
5 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 |
|---|---|
| 3D geometry | 1 |
| Camera Localization | 1 |
| Camera Pose Estimation | 1 |
| Metric Learning | 1 |
| Pose Estimation | 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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