3D Point Cloud Matching

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

3 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 (10 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.

• 3D Point Capsule Networks
  27 Dec 2018 2 repositories listed
  In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
• The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
  16 Nov 2018 2 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
• Lepard: Learning partial point cloud matching in rigid and deformable scenes
  24 Nov 2021 1 repository listed Syntology ran 5 of 14 samples · 9 unverified
  We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes.
• Human Correspondence Consensus for 3D Object Semantic Understanding
  29 Dec 2019 1 repository listed
  Semantic understanding of 3D objects is crucial in many applications such as object manipulation.
• LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
  21 Nov 2019 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching.
• Fully Convolutional Geometric Features
  27 Oct 2019 1 repository listed
  Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking.
• 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.
• Robust Point Set Registration Using Gaussian Mixture Models
  23 Dec 2010 1 repository listed
  Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized.

Syntology lines on 3 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.