3D Point Cloud Interpolation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 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

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (6 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 Spatial-Temporal Embeddings for Sequential Point Cloud Frame Interpolation
  11 Sep 2023 1 repository listed
  A point cloud sequence is usually acquired at a low frame rate owing to the limitations from the sensing equipment.
• Spatial-Temporal Consistency Refinement Network for Dynamic Point Cloud Frame Interpolation
  28 Aug 2023 1 repository listed
  Point cloud frame interpolation aims to improve the frame rate of a point cloud sequence by synthesising intermediate frames between consecutive frames.
• NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation
  27 Mar 2023 1 repository listed
  In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for…
• IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
  22 Mar 2022 1 repository listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
• PointINet: Point Cloud Frame Interpolation Network
  18 Dec 2020 1 repository listed
  Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.

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.