Point Cloud Super Resolution

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

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

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (16 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.

• PU-GAN: a Point Cloud Upsampling Adversarial Network
  25 Jul 2019 3 repositories listed
  Point clouds acquired from range scans are often sparse, noisy, and non-uniform.
• Patch-based Progressive 3D Point Set Upsampling
  27 Nov 2018 3 repositories listed
  We present a detail-driven deep neural network for point set upsampling.
• PU-Net: Point Cloud Upsampling Network
  21 Jan 2018 3 repositories listed
  Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
• Lightweight super resolution network for point cloud geometry compression
  2 Nov 2023 1 repository listed
  This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network.
• ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields
  2 Oct 2023 1 repository listed
  Our proposed implicit occupancy representation enables efficient point classification, effectively discerning points belonging to the surface from non-surface points.
• TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling
  2 Oct 2023 1 repository listed
  TP-NoDe mitigates the need for task-specific training of upsampling networks for a specific upsampling ratio by reusing a point cloud denoising framework.
• PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
  22 Aug 2022 1 repository listed
  The performance of PU-MFA was compared with other state-of-the-art methods through various experiments using the PU-GAN dataset, which is a synthetic point cloud dataset, and the KITTI dataset, which is the real-scanned…
• Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
  9 Feb 2021 1 repository listed
  Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.
• PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
  24 Feb 2020 1 repository listed
  Matrix 𝐓 approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively…
• PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
  30 Nov 2019 1 repository listed
  We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.