Supervised Only 3D Point Cloud Classification

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

1 dataset 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

13 shown of 13 papers with code (13 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.

• Attention Is All You Need
  12 Jun 2017 595 repositories listed Syntology ran 600 of 946 samples · 346 unverified · 451 pointer-only (licence)
  The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
  2 Dec 2016 110 repositories listed Syntology ran 89 of 164 samples · 75 unverified · 90 pointer-only (licence)
  Point cloud is an important type of geometric data structure.
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
  7 Jun 2017 68 repositories listed Syntology ran 36 of 67 samples · 31 unverified · 26 pointer-only (licence)
  By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
• Dynamic Graph CNN for Learning on Point Clouds
  24 Jan 2018 21 repositories listed Syntology ran 16 of 44 samples · 28 unverified · 31 pointer-only (licence)
  Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
• Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
  14 Mar 2023 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with…
• PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
  9 Jun 2022 3 repositories listed Syntology ran 7 of 17 samples · 10 unverified
  In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
• Point Cloud Mamba: Point Cloud Learning via State Space Model
  1 Mar 2024 2 repositories listed Syntology ran 1 of 11 samples · 10 unverified · 11 pointer-only (licence)
  To enable Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method to convert point clouds into 1-D point sequences while ensuring that neighboring points in…
• Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
  23 Apr 2024 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  In contrast, the newly proposed Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity.
• Decoupled Local Aggregation for Point Cloud Learning
  31 Aug 2023 1 repository listed
  In this work, we propose to decouple the explicit modelling of spatial relations from local aggregation.
• Self-positioning Point-based Transformer for Point Cloud Understanding
  29 Mar 2023 1 repository listed Syntology ran 1 of 12 samples · 11 unverified
  In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity.
• Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
  8 Mar 2023 1 repository listed
  To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short.
• Surface Representation for Point Clouds
  11 May 2022 1 repository listed Syntology ran 2 of 15 samples · 13 unverified
  Based on a simple baseline of PointNet++ (SSG version), Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of…
• Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
  15 Feb 2022 1 repository listed Syntology ran 10 of 10 samples · 0 unverified · 2 pointer-only (licence)
  We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical…

Syntology lines on 11 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.