3D Dense Shape Correspondence

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

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

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (11 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.

• Learning elementary structures for 3D shape generation and matching
  13 Aug 2019 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
• Correspondence Learning via Linearly-invariant Embedding
  25 Oct 2020 2 repositories listed
  However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that learning the basis from data can both improve robustness and lead to better…
• Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features
  28 Nov 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  We present Diff3F as a simple, robust, and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds).
• SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
  10 Apr 2023 1 repository listed
  The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of…
• ROCA: Robust CAD Model Retrieval and Alignment from a Single Image
  3 Dec 2021 1 repository listed
  We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image.
• DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
  16 Oct 2021 1 repository listed
  We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction.
• CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds
  31 Dec 2020 1 repository listed
  The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence.
• 3D-CODED : 3D Correspondences by Deep Deformation
  13 Jun 2018 1 repository listed
  By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.

Syntology lines on 2 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.