Browse State-of-the-Art › 3D Canonicalization
3D Canonicalization
3 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
3D Canonicalization is the process of estimating a transformation-invariant feature for classification and part segmentation tasks.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
No dataset record in the archive lists this task.
Subtasks archive 2025-07-28
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Most implemented papers archive 2025-07-28
3 shown of 3 papers with code (3 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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24 Mar 2025 1 repository listedWe present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images.
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1 Jan 2025 1 repository listedWe present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images.
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19 Jan 2022 1 repository listed Syntology ran 0 of 19 samples · 19 unverifiedConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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