Methods › Computer Vision › 3D Reconstruction › CodeSLAM
CodeSLAM
Introduced by Michael Bloesch et al. in CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
CodeSLAM represents the 3D geometry of a scene using the latent space of a variational autoencoder. The depth thus becomes a function of the RGB image and the unknown code, D = G_θ(I,c). During training time, the weights of the network G_θ are learnt by training the generator and encoder using a standard autoencoding task. At test time the code c and the pose of the images is found by optimizing the reprojection error over multiple images.
Papers archive 2025-07-28
2 shown of 2, 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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CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations 19 Jul 2021 · 0 repositories · arXiv:2107.08994
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CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM 3 Apr 2018 · 3 repositories · arXiv:1804.00874Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
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 |
|---|---|
| 3D Reconstruction | 1 |
| Depth Estimation | 1 |
| Scene Understanding | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections